Classification is one of the basic and most important operations that can be used in data science and machine learning applications. Multi-label classification is an extension of the multi-class problem where a set of class labels are associated with a particular instance at a time. In a multiclass problem, a single class label is associated with an instance at a time. However, there are many different stacked ensemble methods that have been proposed and because of the complexity associated with the multi-label problems, there is still a lot of scope for improving the prediction accuracy. In this paper, we are proposing the novel extended multi-tier stacked ensemble (EMSTE) method with label correlation by feature subset selection technique and then augmenting those feature subsets while constructing the intermediate dataset for improving the prediction accuracy in the generalization phase of the stacking. The performance effect of the proposed method has been compared with existing methods and showed that our proposed method outperforms the other methods.
Text document clustering and similarity detection is the major part of document management, where every document should be identified by its key terms and domain knowledge. Based on the similarity, the documents are grouped into clusters. For document similarity calculation there are several approaches were proposed in the existing system. But the existing system is either term based or pattern based. And those systems suffered from several problems. To make a revolution in this challenging environment, the proposed system presents an innovative model for document similarity by applying back propagation time stamp algorithm. It discovers patterns in text documents as higher level features and creates a network for fast grouping. It also detects the most appropriate patterns based on its weight and BPTT performs the document similarity measures. Using this approach, the document can be categorized easily. In order to perform the above, a new approach is used. This helps to reduce the training process problems. The above framework is named as BPTT. The BPTT has implemented and evaluated using dot net platform with different set of datasets.
Multi Label Spatial Semi Supervised Classification using Spatial Associative ...cscpconf
Multi-label spatial classification based on association rules with multi objective genetic
algorithms (MOGA) enriched by semi supervised learning is proposed in this paper. It is to deal
with multiple class labels problem. In this paper we adapt problem transformation for the multi
label classification. We use hybrid evolutionary algorithm for the optimization in the generation
of spatial association rules, which addresses single label. MOGA is used to combine the single
labels into multi labels with the conflicting objectives predictive accuracy and
comprehensibility. Semi supervised learning is done through the process of rule cover
clustering. Finally associative classifier is built with a sorting mechanism. The algorithm is
simulated and the results are compared with MOGA based associative classifier, which out
performs the existing
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
A Formal Machine Learning or Multi Objective Decision Making System for Deter...Editor IJCATR
Decision-making typically needs the mechanisms to compromise among opposing norms. Once multiple objectives square measure is concerned of machine learning, a vital step is to check the weights of individual objectives to the system-level performance. Determinant, the weights of multi-objectives is associate in analysis method, associated it's been typically treated as a drawback. However, our preliminary investigation has shown that existing methodologies in managing the weights of multi-objectives have some obvious limitations like the determination of weights is treated as one drawback, a result supporting such associate improvement is limited, if associated it will even be unreliable, once knowledge concerning multiple objectives is incomplete like an integrity caused by poor data. The constraints of weights are also mentioned. Variable weights square measure is natural in decision-making processes. Here, we'd like to develop a scientific methodology in determinant variable weights of multi-objectives. The roles of weights in a creative multi-objective decision-making or machine-learning of square measure analyzed, and therefore the weights square measure determined with the help of a standard neural network.
A hybrid naïve Bayes based on similarity measure to optimize the mixed-data c...TELKOMNIKA JOURNAL
In this paper, a hybrid method has been introduced to improve the classification performance of naïve Bayes (NB) for the mixed dataset and multi-class problems. This proposed method relies on a similarity measure which is applied to portions that are not correctly classified by NB. Since the data contains a multi-valued short text with rare words that limit the NB performance, we have employed an adapted selective classifier based on similarities (CSBS) classifier to exceed the NB limitations and included the rare words in the computation. This action has been achieved by transforming the formula from the product of the probabilities of the categorical variable to its sum weighted by numerical variable. The proposed algorithm has been experimented on card payment transaction data that contains the label of transactions: the multi-valued short text and the transaction amount. Based on K-fold cross validation, the evaluation results confirm that the proposed method achieved better results in terms of precision, recall, and F-score compared to NB and CSBS classifiers separately. Besides, the fact of converting a product form to a sum gives more chance to rare words to optimize the text classification, which is another advantage of the proposed method.
Text document clustering and similarity detection is the major part of document management, where every document should be identified by its key terms and domain knowledge. Based on the similarity, the documents are grouped into clusters. For document similarity calculation there are several approaches were proposed in the existing system. But the existing system is either term based or pattern based. And those systems suffered from several problems. To make a revolution in this challenging environment, the proposed system presents an innovative model for document similarity by applying back propagation time stamp algorithm. It discovers patterns in text documents as higher level features and creates a network for fast grouping. It also detects the most appropriate patterns based on its weight and BPTT performs the document similarity measures. Using this approach, the document can be categorized easily. In order to perform the above, a new approach is used. This helps to reduce the training process problems. The above framework is named as BPTT. The BPTT has implemented and evaluated using dot net platform with different set of datasets.
Multi Label Spatial Semi Supervised Classification using Spatial Associative ...cscpconf
Multi-label spatial classification based on association rules with multi objective genetic
algorithms (MOGA) enriched by semi supervised learning is proposed in this paper. It is to deal
with multiple class labels problem. In this paper we adapt problem transformation for the multi
label classification. We use hybrid evolutionary algorithm for the optimization in the generation
of spatial association rules, which addresses single label. MOGA is used to combine the single
labels into multi labels with the conflicting objectives predictive accuracy and
comprehensibility. Semi supervised learning is done through the process of rule cover
clustering. Finally associative classifier is built with a sorting mechanism. The algorithm is
simulated and the results are compared with MOGA based associative classifier, which out
performs the existing
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
A Formal Machine Learning or Multi Objective Decision Making System for Deter...Editor IJCATR
Decision-making typically needs the mechanisms to compromise among opposing norms. Once multiple objectives square measure is concerned of machine learning, a vital step is to check the weights of individual objectives to the system-level performance. Determinant, the weights of multi-objectives is associate in analysis method, associated it's been typically treated as a drawback. However, our preliminary investigation has shown that existing methodologies in managing the weights of multi-objectives have some obvious limitations like the determination of weights is treated as one drawback, a result supporting such associate improvement is limited, if associated it will even be unreliable, once knowledge concerning multiple objectives is incomplete like an integrity caused by poor data. The constraints of weights are also mentioned. Variable weights square measure is natural in decision-making processes. Here, we'd like to develop a scientific methodology in determinant variable weights of multi-objectives. The roles of weights in a creative multi-objective decision-making or machine-learning of square measure analyzed, and therefore the weights square measure determined with the help of a standard neural network.
A hybrid naïve Bayes based on similarity measure to optimize the mixed-data c...TELKOMNIKA JOURNAL
In this paper, a hybrid method has been introduced to improve the classification performance of naïve Bayes (NB) for the mixed dataset and multi-class problems. This proposed method relies on a similarity measure which is applied to portions that are not correctly classified by NB. Since the data contains a multi-valued short text with rare words that limit the NB performance, we have employed an adapted selective classifier based on similarities (CSBS) classifier to exceed the NB limitations and included the rare words in the computation. This action has been achieved by transforming the formula from the product of the probabilities of the categorical variable to its sum weighted by numerical variable. The proposed algorithm has been experimented on card payment transaction data that contains the label of transactions: the multi-valued short text and the transaction amount. Based on K-fold cross validation, the evaluation results confirm that the proposed method achieved better results in terms of precision, recall, and F-score compared to NB and CSBS classifiers separately. Besides, the fact of converting a product form to a sum gives more chance to rare words to optimize the text classification, which is another advantage of the proposed method.
Textual Data Partitioning with Relationship and Discriminative AnalysisEditor IJMTER
Data partitioning methods are used to partition the data values with similarity. Similarity
measures are used to estimate transaction relationships. Hierarchical clustering model produces tree
structured results. Partitioned clustering produces results in grid format. Text documents are
unstructured data values with high dimensional attributes. Document clustering group ups unlabeled text
documents into meaningful clusters. Traditional clustering methods require cluster count (K) for the
document grouping process. Clustering accuracy degrades drastically with reference to the unsuitable
cluster count.
Textual data elements are divided into two types’ discriminative words and nondiscriminative
words. Only discriminative words are useful for grouping documents. The involvement of
nondiscriminative words confuses the clustering process and leads to poor clustering solution in return.
A variation inference algorithm is used to infer the document collection structure and partition of
document words at the same time. Dirichlet Process Mixture (DPM) model is used to partition
documents. DPM clustering model uses both the data likelihood and the clustering property of the
Dirichlet Process (DP). Dirichlet Process Mixture Model for Feature Partition (DPMFP) is used to
discover the latent cluster structure based on the DPM model. DPMFP clustering is performed without
requiring the number of clusters as input.
Document labels are used to estimate the discriminative word identification process. Concept
relationships are analyzed with Ontology support. Semantic weight model is used for the document
similarity analysis. The system improves the scalability with the support of labels and concept relations
for dimensionality reduction process.
Fuzzy clustering has been widely studied and applied in a variety of key areas of science and
engineering. In this paper the Improved Teaching Learning Based Optimization (ITLBO)
algorithm is used for data clustering, in which the objects in the same cluster are similar. This
algorithm has been tested on several datasets and compared with some other popular algorithm
in clustering. Results have been shown that the proposed method improves the output of
clustering and can be efficiently used for fuzzy clustering.
This paper presents a review & performs a comparative evaluation of few known machine learning
algorithms in terms of their suitability & code performance on any given data set of any size. In this paper,
we describe our Machine Learning ToolBox that we have built using python programming language. The
algorithms used in the toolbox consists of supervised classification algorithms such as Naïve Bayes,
Decision Trees, SVM, K-nearest Neighbors and Neural Network (Backpropagation). The algorithms are
tested on iris and diabetes dataset and are compared on the basis of their accuracy under different
conditions. However using our tool one can apply any of the implemented ML algorithms on any dataset of
any size. The main goal of building a toolbox is to provide users with a platform to test their datasets on
different Machine Learning algorithms and use the accuracy results to determine which algorithms fits the
data best. The toolbox allows the user to choose a dataset of his/her choice either in structured or
unstructured form and then can choose the features he/she wants to use for training the machine We have
given our concluding remarks on the performance of implemented algorithms based on experimental
analysis
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
A hybrid constructive algorithm incorporating teaching-learning based optimiz...IJECEIAES
In neural networks, simultaneous determination of the optimum structure and weights is a challenge. This paper proposes a combination of teachinglearning based optimization (TLBO) algorithm and a constructive algorithm (CA) to cope with the challenge. In literature, TLBO is used to choose proper weights, while CA is adopted to construct different structures in order to select the proper one. In this study, the basic TLBO algorithm along with an improved version of this algorithm for network weights selection are utilized. Meanwhile, as a constructive algorithm, a novel modification to multiple operations, using statistical tests (MOST), is applied and tested to choose the proper structure. The proposed combinatorial algorithms are applied to ten classification problems and two-time-series prediction problems, as the benchmark. The results are evaluated based on training and testing error, network complexity and mean-square error. The experimental results illustrate that the proposed hybrid method of the modified MOST constructive algorithm and the improved TLBO (MCO-ITLBO) algorithm outperform the others; moreover, they have been proven by Wilcoxon statistical tests as well. The proposed method demonstrates less average error with less complexity in the network structure.
Most of the text classification problems are associated with multiple class labels and hence automatic text
classification is one of the most challenging and prominent research area. Text classification is the
problem of categorizing text documents into different classes. In the multi-label classification scenario,
each document is associated may have more than one label. The real challenge in the multi-label
classification is the labelling of large number of text documents with a subset of class categories. The
feature extraction and classification of such text documents require an efficient machine learning algorithm
which performs automatic text classification. This paper describes the multi-label classification of product
review documents using Structured Support Vector Machine.
Review of Various Text Categorization Methodsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Learning to rank image tags with limited training examplesCloudTechnologies
We are the company providing Complete Solution for all Academic Final Year/Semester Student Projects. Our projects are
suitable for B.E (CSE,IT,ECE,EEE), B.Tech (CSE,IT,ECE,EEE),M.Tech (CSE,IT,ECE,EEE) B.sc (IT & CSE), M.sc (IT & CSE),
MCA, and many more..... We are specialized on Java,Dot Net ,PHP & Andirod technologies. Each Project listed comes with
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Database 5. Screen-shots 6. Video File
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Contact:-8121953811,8522991105.040-65511811
cloudtechnologiesprojects@gmail.com
http://cloudstechnologies.in/
USING ONTOLOGIES TO IMPROVE DOCUMENT CLASSIFICATION WITH TRANSDUCTIVE SUPPORT...IJDKP
Many applications of automatic document classification require learning accurately with little training
data. The semi-supervised classification technique uses labeled and unlabeled data for training. This
technique has shown to be effective in some cases; however, the use of unlabeled data is not always
beneficial.
On the other hand, the emergence of web technologies has originated the collaborative development of
ontologies. In this paper, we propose the use of ontologies in order to improve the accuracy and efficiency
of the semi-supervised document classification.
We used support vector machines, which is one of the most effective algorithms that have been studied for
text. Our algorithm enhances the performance of transductive support vector machines through the use of
ontologies. We report experimental results applying our algorithm to three different datasets. Our
experiments show an increment of accuracy of 4% on average and up to 20%, in comparison with the
traditional semi-supervised model.
Seeds Affinity Propagation Based on Text ClusteringIJRES Journal
The objective is to find among all partitions of the data set, best publishing according to some quality measure. Affinity propagation is a low error, high speed, flexible, and remarkably simple clustering algorithm that may be used in forming teams of participants for business simulations and experiential exercises, and in organizing participant’s preferences for the parameters of simulations. This paper proposes an efficient Affinity Propagation algorithm that guarantees the same clustering result as the original algorithm after convergence. The heart of our approach is (1) to prune unnecessary message exchanges in the iterations and (2) to compute the convergence values of pruned messages after the iterations to determine clusters.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
More Related Content
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Textual Data Partitioning with Relationship and Discriminative AnalysisEditor IJMTER
Data partitioning methods are used to partition the data values with similarity. Similarity
measures are used to estimate transaction relationships. Hierarchical clustering model produces tree
structured results. Partitioned clustering produces results in grid format. Text documents are
unstructured data values with high dimensional attributes. Document clustering group ups unlabeled text
documents into meaningful clusters. Traditional clustering methods require cluster count (K) for the
document grouping process. Clustering accuracy degrades drastically with reference to the unsuitable
cluster count.
Textual data elements are divided into two types’ discriminative words and nondiscriminative
words. Only discriminative words are useful for grouping documents. The involvement of
nondiscriminative words confuses the clustering process and leads to poor clustering solution in return.
A variation inference algorithm is used to infer the document collection structure and partition of
document words at the same time. Dirichlet Process Mixture (DPM) model is used to partition
documents. DPM clustering model uses both the data likelihood and the clustering property of the
Dirichlet Process (DP). Dirichlet Process Mixture Model for Feature Partition (DPMFP) is used to
discover the latent cluster structure based on the DPM model. DPMFP clustering is performed without
requiring the number of clusters as input.
Document labels are used to estimate the discriminative word identification process. Concept
relationships are analyzed with Ontology support. Semantic weight model is used for the document
similarity analysis. The system improves the scalability with the support of labels and concept relations
for dimensionality reduction process.
Fuzzy clustering has been widely studied and applied in a variety of key areas of science and
engineering. In this paper the Improved Teaching Learning Based Optimization (ITLBO)
algorithm is used for data clustering, in which the objects in the same cluster are similar. This
algorithm has been tested on several datasets and compared with some other popular algorithm
in clustering. Results have been shown that the proposed method improves the output of
clustering and can be efficiently used for fuzzy clustering.
This paper presents a review & performs a comparative evaluation of few known machine learning
algorithms in terms of their suitability & code performance on any given data set of any size. In this paper,
we describe our Machine Learning ToolBox that we have built using python programming language. The
algorithms used in the toolbox consists of supervised classification algorithms such as Naïve Bayes,
Decision Trees, SVM, K-nearest Neighbors and Neural Network (Backpropagation). The algorithms are
tested on iris and diabetes dataset and are compared on the basis of their accuracy under different
conditions. However using our tool one can apply any of the implemented ML algorithms on any dataset of
any size. The main goal of building a toolbox is to provide users with a platform to test their datasets on
different Machine Learning algorithms and use the accuracy results to determine which algorithms fits the
data best. The toolbox allows the user to choose a dataset of his/her choice either in structured or
unstructured form and then can choose the features he/she wants to use for training the machine We have
given our concluding remarks on the performance of implemented algorithms based on experimental
analysis
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
A hybrid constructive algorithm incorporating teaching-learning based optimiz...IJECEIAES
In neural networks, simultaneous determination of the optimum structure and weights is a challenge. This paper proposes a combination of teachinglearning based optimization (TLBO) algorithm and a constructive algorithm (CA) to cope with the challenge. In literature, TLBO is used to choose proper weights, while CA is adopted to construct different structures in order to select the proper one. In this study, the basic TLBO algorithm along with an improved version of this algorithm for network weights selection are utilized. Meanwhile, as a constructive algorithm, a novel modification to multiple operations, using statistical tests (MOST), is applied and tested to choose the proper structure. The proposed combinatorial algorithms are applied to ten classification problems and two-time-series prediction problems, as the benchmark. The results are evaluated based on training and testing error, network complexity and mean-square error. The experimental results illustrate that the proposed hybrid method of the modified MOST constructive algorithm and the improved TLBO (MCO-ITLBO) algorithm outperform the others; moreover, they have been proven by Wilcoxon statistical tests as well. The proposed method demonstrates less average error with less complexity in the network structure.
Most of the text classification problems are associated with multiple class labels and hence automatic text
classification is one of the most challenging and prominent research area. Text classification is the
problem of categorizing text documents into different classes. In the multi-label classification scenario,
each document is associated may have more than one label. The real challenge in the multi-label
classification is the labelling of large number of text documents with a subset of class categories. The
feature extraction and classification of such text documents require an efficient machine learning algorithm
which performs automatic text classification. This paper describes the multi-label classification of product
review documents using Structured Support Vector Machine.
Review of Various Text Categorization Methodsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Learning to rank image tags with limited training examplesCloudTechnologies
We are the company providing Complete Solution for all Academic Final Year/Semester Student Projects. Our projects are
suitable for B.E (CSE,IT,ECE,EEE), B.Tech (CSE,IT,ECE,EEE),M.Tech (CSE,IT,ECE,EEE) B.sc (IT & CSE), M.sc (IT & CSE),
MCA, and many more..... We are specialized on Java,Dot Net ,PHP & Andirod technologies. Each Project listed comes with
the following deliverable: 1. Project Abstract 2. Complete functional code 3. Complete Project report with diagrams 4.
Database 5. Screen-shots 6. Video File
SERVICE AT CLOUDTECHNOLOGIES
IEEE, WEB, WINDOWS PROJECTS ON DOT NET, JAVA& ANDROID TECHNOLOGIES,EMBEDDED SYSTEMS,MAT LAB,VLSI DESIGN.
ME, M-TECH PAPER PUBLISHING
COLLEGE TRAINING
Thanks&Regards
cloudtechnologies
# 304, Siri Towers,Behind Prime Hospitals
Maitrivanam, Ameerpet.
Contact:-8121953811,8522991105.040-65511811
cloudtechnologiesprojects@gmail.com
http://cloudstechnologies.in/
USING ONTOLOGIES TO IMPROVE DOCUMENT CLASSIFICATION WITH TRANSDUCTIVE SUPPORT...IJDKP
Many applications of automatic document classification require learning accurately with little training
data. The semi-supervised classification technique uses labeled and unlabeled data for training. This
technique has shown to be effective in some cases; however, the use of unlabeled data is not always
beneficial.
On the other hand, the emergence of web technologies has originated the collaborative development of
ontologies. In this paper, we propose the use of ontologies in order to improve the accuracy and efficiency
of the semi-supervised document classification.
We used support vector machines, which is one of the most effective algorithms that have been studied for
text. Our algorithm enhances the performance of transductive support vector machines through the use of
ontologies. We report experimental results applying our algorithm to three different datasets. Our
experiments show an increment of accuracy of 4% on average and up to 20%, in comparison with the
traditional semi-supervised model.
Seeds Affinity Propagation Based on Text ClusteringIJRES Journal
The objective is to find among all partitions of the data set, best publishing according to some quality measure. Affinity propagation is a low error, high speed, flexible, and remarkably simple clustering algorithm that may be used in forming teams of participants for business simulations and experiential exercises, and in organizing participant’s preferences for the parameters of simulations. This paper proposes an efficient Affinity Propagation algorithm that guarantees the same clustering result as the original algorithm after convergence. The heart of our approach is (1) to prune unnecessary message exchanges in the iterations and (2) to compute the convergence values of pruned messages after the iterations to determine clusters.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Gen AI Study Jams _ For the GDSC Leads in India.pdf
Multi-label learning by extended multi-tier stacked ensemble method with label correlated feature subset augmentation
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 3, June 2023, pp. 3384∼3397
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i3.pp3384-3397 ❒ 3384
Multi-label learning by extended multi-tier stacked
ensemble method with label correlated feature subset
augmentation
Hemavati1
, Visweswariah Susheela Devi2
, Ramalingappa Aparna1
1Department of Information Science and Engineering, Siddaganga Institute of Technology, Tumakuru, India
2Department of Computer Science and Automation, Indian Institute of Science, Bengaluru, India
Article Info
Article history:
Received Apr 16, 2022
Revised Sep 23, 2022
Accepted Oct 1, 2022
Keywords:
Classification
Correlation
Ensemble learning
Stacking
ABSTRACT
Classification is one of the basic and most important operations that can be used
in data science and machine learning applications. Multi-label classification is
an extension of the multi-class problem where a set of class labels are associ-
ated with a particular instance at a time. In a multiclass problem, a single class
label is associated with an instance at a time. However, there are many different
stacked ensemble methods that have been proposed and because of the com-
plexity associated with the multi-label problems, there is still a lot of scope for
improving the prediction accuracy. In this paper, we are proposing the novel
extended multi-tier stacked ensemble (EMSTE) method with label correlation
by feature subset selection technique and then augmenting those feature subsets
while constructing the intermediate dataset for improving the prediction accu-
racy in the generalization phase of the stacking. The performance effect of the
proposed method has been compared with existing methods and showed that our
proposed method outperforms the other methods.
This is an open access article under the CC BY-SA license.
Corresponding Author:
Hemavati
Department of Information Science and Engineering, Siddaganga Institute of Technology
Tumakuru, India
Email: hemavd@sit.ac.in
1. INTRODUCTION
Classification is one of the basic and most important operations that can be used in data science and
machine learning applications. Multi-label classifications are found in the areas of text categorization [1],
medical applications [2], movie genres classification [3], protein classification [4] and so on. The classification
of multi-label data is entirely different from the binary and multi-class problems, since the classification of
multi-label data may get affected by the inherently hidden label correlation [5]. There are a substantial number
of proposed algorithms for dealing with these kinds of problems. In [6], these are categorized into two different
approaches. They are problem transformation and algorithm adaptation approach. problem transformation
(PT)approach, data is adapted according to the algorithm and the problem is decomposed into multiple binary
class or multi-class problems. The different transformation methods include: binary relevance (BR), ranking
by pairwise comparison (RPC), label powerset (LP) method, and calibrated label ranking (CLR).
BR [7]–[9]: This method converts the original problem of multi-label into m number of binary prob-
lems by containing all examples of the original dataset Dj, j = 1...m, and learning happens with each label.
The final output will be the union of all labels predicted by m classifiers. The main drawback of the BR method
Journal homepage: http://ijece.iaescore.com
2. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 3385
is that it ignores label correlation between the labels. LP [10] method considers the combination of unique label
sets present in the original training dataset as a single class label in the transformed dataset. The output for the
new instance will be one of these unique label sets with the highest probability or label ranking. LP suffers from
the problem of computation complexity, (n, 2L
) where n indicates the total number of data points and L is the
number of labels. Another problem is that it can only predict the unique label combinations which are present
in the training dataset, which may lead to a problem of class imbalance. The pruned PT method [10] provides
the solution for the second problem indicated above by removing the label sets which are occurring rarer than a
user-defined threshold, and often changing by replacing them with decomposed subsets of these corresponding
set of labels which are present whose value is greater than the peak value in a dataset. Ranking by pairwise
comparison (RPC) [11] transforms the dataset of multi-label into L(L − 1)/2 binary label datasets where L
indicates the total number of labels, one for each pair of labels. Each dataset keeps as it is the data points
from the original dataset, that may be associated with at least one of the corresponding labels but not both.
However, ranking keeps a corresponding order of the labels. In [12], the argument is that such kind of ranking
may not hold a usual “zero-point” and hence, does not give any kind of knowledge about the preference or the
absolute choice, which can distinguish among all other alternative options. Later, by proposing an extension of
RPC, that is, calibrated label ranking (CLR), with an extra label to the original or the base label set, that can
be considered as a “neutral breaking point” between related and unrelated class labels. Algorithm adaptation
approach adapts the base algorithms to work on multi-label datasets without using any transformation method.
Some of the algorithms like AdaBoost.MH [13], MLkNN [14], and random k-labelsets (RAkEL) [15] can be
used directly on multi-label data.
2. LITERATURE REVIEW
This section gives the background of different existing methods for multi-label classification. The
review of existing methods helps to improve the new methods and the usage of the methodologies provides
more clarity on the novelty of new methods. This section also gives a comparative study of existing methods.
2.1. Multi-label learning with label correlation and feature selection method
Exploiting the label correlation is one of the research issues to be considered for improving the multi-
label classification performance. In the literature, we can find many related approaches that have been proposed
in the past decades for exploiting label correlation and feature selection. There are three categories of label cor-
relation strategies: one is the first-order strategy that ignores the label correlation. One can understand how
this strategy is achieved from the BR example considered. But, there are advantages with the first-order strat-
egy concerning implementation simplicity and efficiency, but it will not consider label correlation information.
Another strategy is the second-order strategy. Here, the pairwise correlation is considered between the labels.
For example, in LR proposed in [16] and an extended LR proposed in [17], a virtual label is added to each
instance i.e., CLR. In [18], a conventional support vector machine (SVM) is modified based on ranking loss by
considering a new loss term which is called RankSVM. In the work carried out by Zhi-Fen He et al. [19], label
correlations with missing labels are considered. The last one is the high order strategy. Here, among the labels,
the high order correlations will be exploited. For instance, the label powerset (LP) [20] considers the combina-
tion of unique label sets present in the original training dataset as a single class label in the transformed dataset.
The output for the new instance will be one of these unique label sets with the highest probability or label
ranking. The PPT method overcomes the problem of LP by the selection of a small random subset of labels,
then trains an ensemble of classifiers by using the LP method. But, it only addresses the implicit correlation
among labels which considers the weight vector by learning each classifier depending on the weight vectors
of other classifiers, the matrix of correlated labels, and the parameters of the model. As we know, multi-label
learning has many problems concerning label correlation, and it may not fit well for all real-time applications
where the overall importance of label distribution matters. Geng [21] have proposed label distribution learning
(LDL) for such problems. The LDL covers some of the labels, which will represent the degree of the instance.
LDL could improve the learning process in case of utilization of the label correlation concerning facial depres-
sion recognition as contributed by Zhou et al. [22], facial age estimation by Wen et al. [23] and many other
applications.
Multi-label learning by extended multi-tier stacked ensemble method with label correlated ... (Hemavati)
3. 3386 ❒ ISSN: 2088-8708
2.2. Ensemble learning
Ensemble methods are useful when it requires to take a decision based on collective opinion of dif-
ferent sources. The result of the base classifier can be used in constructing the next-level classifier which are
considered as dependent ensemble methods. The AdaBoost algorithm is useful for improving the performance
of various machine learning algorithms or weak learners. All data points in the training dataset are weighted.
The initial weight is set to: W(Xi) = 1/n where Xi is an example of ith
and n indicates the total number
of data points with multiple labels in the training dataset. Methods of meta-learning are particularly useful
for those which are categorized by always correctly classifying or incorrectly classifying certain conditions.
Some of the methods for measuring are distribution weight, majority vote, Bayesian combination, performance
weight, and entropy weight. In our paper, we use a meta-learning method called Stacking. This method is used
to accomplish greater accuracy with maximal generalization. It is often used to integrate models created by
distinct classifiers to form a meta dataset. That is, rather than using input features from the original training
dataset, the input features will be the predicted results of its base classifiers and class labels will be considered
as such in the original training dataset. Later, the meta classifier will produce the final prediction from the meta
dataset. The weighted classifier selection has been proposed by Xia et al. [24], with sparsity regularization for
ensemble member construction for multi-label classification.
2.3. Research targets
The existing algorithms which are mentioned above are developed to find out the solution for the
multi-label classification problems. But still, there are challenges related to label correlation, the curse of di-
mensionality, and label imbalance. There exist many ensemble techniques which improve the performance of
learning through different combination approaches. These combination approaches are mainly used to concen-
trate on the model overfitting and the sensitivity of initialization. Our contributions are summarized as: i) we
propose a novel extended multi-tier stacked ensemble method for multi-label classification, which is useful in
constructing the strong meta-learning phase by considering different combination schemes for model setting;
ii) we also consider the label correlation feature subset augmentation for improving the classification perfor-
mance; and iii) to the best of our knowledge, this approach works significantly more fine than the existing
systems.
The experiments have been carried out on 10 different multi-label datasets of different domains. The
rest of the paper is organized as. In section 2, we describe the proposed method, in section 3 we present
the mathematical model, in section 4 we will be discussing evaluation metrics and in section 5 we discuss
experiments, followed by the conclusion.
3. PROPOSED METHOD
The main challenge with the stacked ensemble method is that the variance of the base tier may be
inherited to level1, i.e., meta learner. One more problem associated with the multi-label data is the ignorance of
label correlation information when applied with the binary relevance method. In this paper, we are considering
the label correlated feature subset for augmenting with the ensemble tier. In the literature, many different ways
have been discussed for stacking and label correlation methods and those approaches have shown improved re-
sults for the performance metrics, but still, there is a lot of scope for improving the performance by considering
the label correlation with multi-tier stacked ensemble method. Figure 1 shows the extended multi-tier stack
method. The base level uses predefined base classifiers for the given problem. The combination schemes are
then used to get the combined prediction. So, the bias and variance can be controlled in these two tiers. The
novelty we are introducing is that we are augmenting the meta dataset constructed in the generalization tier
with the label correlated feature subset. Using the label correlation helps to improve the classification accuracy.
4. MATHEMATICAL MODEL
In this section, we discuss the mathematical model, which gives the process of creating a real-world
representation of the proposed system required in this paper. The representation of the proposed model in a
mathematical model helps to transform the description into an accurate presentation of the proposed model.
Table 1 gives the description of notations used in this paper.
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Figure 1. Architecture of multi-tier stacked ensemble with correlated feature subset augmentation method for
multi-label data
4.1. Base-tier
Let D = {x, y} where x ∈ X and y ⊆ Y , X = {x1, x2, ..., xn} and Y = {y1, y2, ..., yL} and each
yj = {0, 1} and j = 1, 2, ..., L. Then, train the classifier with ℏ(X) to predict the label subset Y ′
, that is,
Y ′
← ℏ(X) where Y ′
∈ {0, 1}L
. Let H = {ℏ1, ℏ2..., ℏm} be set of base classifiers and each base classifier
predicts the set of labels which can be mapped on the label set Y .
Y ′
= ℏk(X) (1)
where ℏk ∈ H and k = {1, 2, 3, ..., m} let BL = {BL1, BL2, ..., BLm} be the set of label sets predicted by
each base classifier.
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BLk = Y ′
← ℏk(X) (2)
Table 1. Notations
Symbols Descriptions
D Training dataset
X Feature set of training dataset
Y Label set of training dataset
n Number of features
xi ith instance
yj jth label
hk(X) kth base classifier
Y ′ Predicted label set
H Base classifier set
m Number of classifiers
BLk Set of Predicted label set
WLk Set of combination methods
Si Semantic label set
Fk Features of semantic label set
(Fk; Si) Relatedness among feature of candidate set
E(Fk) Function for evaluation Mx(Fk, L)
4.2. Ensemble tier
Let WL = {WL1, WL2, ..., WLN } be the set of combination methods for combining the labels pre-
dicted by the base classifiers to map them with the proper subset of the label set. Consider the following
(3):
WLk : BLk → Y ′
(3)
and Y ′
∈ {0, 1}L
WLk = Aggr(ℏk(X)) (4)
where k = {1, 2, 3, ..., m}
4.3. Generalization tier
In this step, we are selecting maximum label correlated feature subset for augmenting with ensemble
dataset. There are many feature selection methods for multi-label dataset such as D2F, SCLS and MDMR
methods. These feature selection methods use low order similarities to figure out candidate features [25]–[27].
In this paper, we are making use of considering max-correlation (MCMFS) proposed by Zhang et al. [28]
algorithm, which uses max correlation between the labels. In this method, the label set is divided into m
semantic groups L′
= {s1, s2, ..., sm}, where each si = {li1, li2, ..., liq} ∈ Y and i = 1, 2...m and the
following condition is satisfied, i.e., s1 ∪ s2 ∪ .. ∪ sm = L and si ∩ sj =. Then the (Fk; Si) indicates
relatedness among feature of candidate set and the label sets in each semantic group is defined as (5).
(Fk; Si) = max(µ(Fk; yj)) (5)
In (5) a larger value indicates the candidate feature which is of more relevance in the semantic group and a
small value indicates a weak relevance in the semantic group. Then we can obtain the maximum correlation of
d dimensional vector of feature Fk and the label set, L i.e.,
Cor(fk; L′
) = [(Fk; s1), (Fk, s2)...(Fk, sm)] (6)
then
Mx(Fk, L) = max
si∈L′
(Fk, si) (7)
Mx(Fk, L) = max
si∈L′
{max
lj ∈si
µ(Fk, lj)} (8)
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6. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 3389
Mx(Fk, L) = max
lj ∈L
µ(Fk, lj) (9)
where Mx(Fk, L) takes the maximum relevance of the corresponding features effectively with respect to label
set, irrespective of any number of labels that are present in the semantic group. Mx(Fk, L) can exactly make
a selection of the critical features. Based on this, we can use MCMFS as feature subset selection algorithm for
selecting feature subset. The function for evaluation is given as (10) and (11):
E(Fk) = Mx(Fk, L) −
1
|s|
Σµ(Fk; Fj) (10)
E(Fk) = max
lj ∈L
µ(Fk, lj) −
1
|s|
Σµ(Fk; Fj) (11)
after selecting the feature subset, Fs, it has to be augmented with the predicted result of the ensemble method.
D′
= WL ∪ Fs ∪ Y (12)
where WL is the predicted result of the ensemble classifier, Fs is the label correlated feature subset and Y is
the label set from the original training dataset. After constructing meta dataset, we apply the meta learner for
final model training.
fin model ← ℏ(D′
) (13)
Algorithm 1 uses the base classifiers for predicting the first level prediction. We use binary relevance
for the problem transformation and then, for the base classification we have chosen naive Bayes, SVM, decision
tree classifiers. Algorithm 2 depicts applying the combination schemes for the predicted result from the base
classifier. Here we are considering bagging and boosting combination schemes for stacking. In Algorithm 3
we are using the MCMFS algorithm for feature subset selection in our model for selecting the critical features
for augmenting in the generalization tier. Algorithm 4 depicts the generalization tier, where the meta dataset
is constructed by combining the predicted result, feature subset, and the label set from the original training
dataset. Once the meta dataset is constructed, the meta learner is applied for the final prediction.
Algorithm 1 Classification with base classifier by using transformation method
1: Input: D = {Xi, Y i}n
i=1, where Xi ∈ Rn, Y i ⊆ Y (set of all labels)
2: Output: Stacked Ensemble classifier
begin
3: Step 1: Transform the multi-label data to binary class problem by applying binary relevance (BR) method.
4: DBR ← BR(D)
5: Step 2: Apply the base classifiers on DBR
6: for i ← 1 to m
7: Train the base classifier ℏi(X) based on DBR.
8: end for
9: BL = {ℏ1(X), ℏ2(X), ..., ℏm(X)}
10: Step 3: return BL
end
Algorithm 2 Applying the combination scheme for the predicted result from the base classifier
1: Input: The predicted result of the base classifiers BL
2: Output: new ensemble dataset WL
begin
3: Step 1: apply the combination schemes (CS) to produce ensemble prediction.
4: for j ← 1 to p
5: for i ← 1 to m
6: WLi = CSj(BLi)
7: end for
8: end for
9: Step 2: return Aggr(WL)
end
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Algorithm 3 Selecting the label correlated feature subset by making use of the algorithm MCMFS [28]
1: Input: Full feature set from the original training dataset F = {f1, f2...fd} and label set L = {l1, l2...lm}
2: Output: Correlated feature subset Fs
begin
3: Fs ← Null
4: p ← 0
5: for i ← 1 to d
6: calculate Mx(Fi, L) using eq(9)
7: end for
8: while p < b do
9: if p == 0 then
10: select fj with highest Mx(Fi, L)
11: p = p + 1
12: Fs = Fs ∪ {fj}
13: F = F − {fj}
14: end if
15: for each fi ∈ F do
16: calculate µ(fi; fj)
17: by using equation (11) calculate E(Fi)
18: end for
19: with the highest E(Fi), select the feature fj
20: Fs = Fs ∪ {fj}
21: F = F − {fj}
22: end while
end
Algorithm 4 Constructing meta dataset and applying meta classifier for final prediction
1: Input: The predicted result of the ensemble classifiers WLand the label correlated feature subset Fs and Y .
2: Output: Final prediction
begin
3: Step1: Construct the meta dataset by augmenting the Fs and Y with WL .
4: D′ = WL ∪ Fs ∪ Y
5: Step2: train the model with new meta dataset D′ with meta learner
6: fin model ← ℏ(D′)
end
5. EVALUATION METRICS
In the multi-label classification method, the output is a set of labels, so the normal evaluation metrics
used for analyzing single-label classification algorithms cannot be applied directly. The following list includes
some of the commonly used evaluation metrics. The metrics used for evaluation differs according to the nature
of the target problem [29].
Precision =
1
N
N
X
i=1
|f(Xi) ∩ Y i|
|f(Xi)|
(14)
Recall =
1
N
N
X
i=1
|f(Xi) ∩ Y i|
|Y i|
(15)
F1 − Measure =
1
N
N
X
i=1
2 ∗ |f(Xi) ∩ Y i|
|Y i| + |f(Xi)|
(16)
A higher the value indicates better performance of an algorithm.
Hamming Loss =
1
N
N
X
i=1
|f(Xi) ⊕ Y i|
|Y i| + |f(Xi)|
(17)
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The performance of the algorithm increases with the decrease in value of Hamming loss, i.e., zero
indicates a perfect classification. The measures such as precision, recall, F1-measure, or any measure which can
be used to evaluate binary classification can be computed on a per-label basis and used for multi-label evaluation
after averaging over all possible labels. The difference between macro-averaging and micro-averaging is that
macro-averaging gets affected by classes with fewer instances, but micro-averaging is affected by classes with
numerous instances.
Macro F1:
Fl
1 = 2
N
X
i=1
(Y l
i ∗ f(Xi)l
)/(
N
X
i=1
Y l
i +
N
X
i=1
f(Xi)l
) (18)
macroF1 =
h
X
l=1
Fl
1 (19)
Where Y l
i is 1 if instance Xi originally has a label l. f(Xi) is the predicted label of instance Xi, f(Xi)l
is 1 if
Xi is predicted to have label l, both Y l
i and f(Xi)l
will have a value 0 otherwise.
MicroF1 =
Ph
i=1
PN
j=1 Y i
j f(X)i
j
Ph
i=1
PN
j=1 Y i
j +
Ph
i=1
PN
j=1 f(X)i
j
(20)
6. RESULTS AND DISCUSSION
6.1. Datasets
For finding out the performance of multi-tier stacked ensemble (EMSTE) we have selected the 10 most
common data sets from Mulan and Meka Repository [30] of standard multi-label datasets and each column of
Table 2 shows the characteristics of the data sets. Here we tried to cover all datasets from different domain. We
have tested whether the proposed method works on all of these different domain datasets.
Table 2. Description of the datasets
Dataset Name #instances #Features #Labels
yeast 2417 103 14
scene 2407 294 6
emotions 593 72 6
music 592 77 6
corel5k 5000 499 374
enron 1702 1001 53
bibtex 7395 1836 159
flags 194 26 12
birds 322 260 20
genbase 662 1186 27
6.2. Baseline methods
The proposed method will be compared with the following baseline ensemble multi-label classification
methods which are related to different concepts like ensemble, stacking and multi-label.
a. Ensemble binary relevance (EBR) method [31]: it is an ensemble version of the binary relevance model,
and it does not consider the label correlation.
b. Ensemble classifier chain (ECC) method [32]: an ensemble version of classifier chain. In this method, the
global label correlation will be considered and the order of chain for each CC will be generated randomly.
c. Ensemble label powerset (EPS) [33] method: it is an improved version of the ensemble Label Power set
method, which focuses on the most important correlation of labels by pruning rarely occurring label sets.
d. RAkEL: It is an improved version of the ensemble of label powerset based on a random small subset of k
labels and the relationship between this small subset of k labels.
e. ADABOOST.MH: a weighted ensemble version based on BR that not only maintains a set of weights over
the instances as AdaBoost does, but also over the labels.
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f. MLS [34] it is a stacked multi-label ensemble version based on binary relevance. The global correlation
between the labels will be considered at the meta level, but misses the local pairwise relationship between
the labels.
g. MLkNN [35] it is a lazy learning multi-label version of kNN algorithm, which is based on statistical infor-
mation taken from the label sets which are neighboring instances of unseen labels. ML-kNN makes use of
maximum a posterior principle to find out the label set for the unseen instances.
6.3. Experimental setup
The experiment has been done by using python3 as well as MEKA [36] tool to verify the performance
of our EMTSE scheme with the existing schemes, which provides the multi-label methods. We have tested the
proposed system on 10 benchmark datasets of different domains for the effectiveness of the proposed system.
In practice, the 10-fold cross-validation is applied for performance evaluation. The random division of all
samples is done in 10 equal parts. Each part is held for testing and the remaining part of the data is merged with
the training dataset. The validation has run ten times in an iterative manner. Then the average performance of
the evaluation metrics is calculated for the given model. Binary relevance decision tree (BRDT) [37] is used
for improved generalization capability of the model. It finds one decision tree for each label. For each label
associated with label set Y , the binary naive Bayes classifier is learned. This is called binary relevance naive
Bayes (BRNB) [38]. This method outputs a set of labels which is the union of different single-label naive
bayes classifiers. In SVM kernel [39], [40] the multi-label problem is decomposed into multi-class problem.
Then it tries to determine the optimal hyperplane based on training data, which is used to classify test data
points or the new data points. In two dimensions, the hyperplane is a simple line. The combination schemes we
selected for experimentation are bagging and boosting. The label correlated feature selection is a method used
to select the top-ranked features for augmenting in the ensemble tier for improving the prediction performance
of the meta learner. We have used MLkNN classifier in the generalization tier as a meta classifier for the final
prediction since MLkNN uses the approach of maximum a-posteriori (MAP) combined with k-NN, and it is
been proved that MLkNN performed far better than several other algorithms [41]. The experimental results
have been recorded in Tables 3–7 with the corresponding rank value among the algorithms and ↑ indicates that
the higher the value, better the performance and ↓ indicates lower the value better the performance.
Table 3. Average precision of different multi-label classification algorithms ↑
Dataset EBR ECC EPS RAkEL Adaboost.MH MLkNN MLS EMSTE
yeast 0.654(3) 0.562(8) 0.573(7) 0.580(6) 0.638(5) 0.701(2) 0.642(4) 0.772(1)
scene 0.635(8) 0.654(5) 0.729(3) 0.711(4) 0.651(7) 0.738(2) 0.671(6) 0.751(1)
emotions 0.735(4) 0.637(6) 0.571(8) 0.581(7) 0.746(3) 0.708(5) 0.732(2) 0.756(1)
music 0.755(2) 0.615(7) 0.595(8) 0.689(4) 0.623(6) 0.658(5) 0.715(3) 0.801(1)
corel5k 0.174(6) 0.167(7) 0.160(8) 0.279(3) 0.294(2) 0.228(5) 0.342(1) 0.251(4)
enron 0.163(8) 0.187(5) 0.170(6) 0.168(7) 0.191(4) 0.238(1) 0.237(2) 0.208(3)
bibtex 0.143(7) 0.114(8) 0.180(5) 0.175(6) 0.198(4) 0.338(1) 0.250(3) 0.251(2)
flags 0.622(6) 0.774(2) 0.542(8) 0.562(7) 0.701(5) 0.738(3) 0.721(4) 0.851(1)
birds 0.231(5) 0.324(4) 0.218(6) 0.475(2) 0.488(1) 0.210(7) 0.701(2) 0.422(3)
genbase 0.798(2) 0.789(4) 0.799(1) 0.727(7) 0.714(8) 0.738(6) 0.797(3) 0.780(5)
Average score 0.491(6) 0.4823(7) 0.4537(8) 0.4947(5) 0.5244(4) 0.5295(3) 0.5808(2) 0.5843(1)
6.4. Experimental results and analysis
Tables 3-7 show the average precision, F1score, MAcroF1, MicroF1, and Hamming loss respectively
of the various methods. It can be seen in many of the cases our method (EMSTE) gives the best result. Among
the other cases, mostly EMSTE is in second place. Even if EMSTE is not in second place, its value is close to
the best value. We are using the Friedman test Fr [42] to analyze the performance of different algorithms based
on the evaluation metrics. The Friedman test Fr and critical value with the significance level α = 0.05 have
been considered. The rejection or null hypothesis is for the equal performance of each metric. As long as we
have two or more populations, and among them, some are not normal, we can make use of the non-parametric
Friedman test considering it an omnibus test to find out the existence of any significant differences among the
median values of the populations. We are using the post-hoc Nemenyi test [43] to retrieve which differences
are significant. If the differences in the mean rank are greater than the critical distance CD=3.32 of the Ne-
menyi test, then we can say that differences among the populations are significant. The statistical analysis was
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10. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 3393
conducted for k=7 (No. of algorithms used for comparison), N=10 (no. of datasets) significance level of the
tests alpha=0.050 with the family-wise. The algorithms which are connected within the CD line are not statis-
tically significantly better than each other. The results of these tests are shown in Figure 2 as critical distance
diagrams. In the diagram, the top-ranked algorithms are on the left side of the CD diagram and the algorithms
which are on the right side of the CD line are low ranked algorithms. For example, in Figure 2(a) EMSTE,
ECC, EBR, and MLkNN are left side of the CD line and are considered to be the top-ranked algorithms, and
RAkEL, EPS, and ADABOOST fall on the right side the of the CD line and are considered to be the low rank-
ing algorithms for the Average Precision performance metrics. Similarly, for the other performance metrics of
the CD diagrams which are shown as Figure 2(b) F1, 2(c) macro F1, 2(d) Micro F1, and 2(e) Hamming loss,
holds the same.
Table 4. F1-score of different multi-label classification algorithms ↑
Dataset EBR ECC EPS RAkEL Adaboost.MH MLkNN MLS EMSTE
yeast 0.385(6) 0.398(5) 0.374(8) 0.383(7) 0.405(3) 0.401(4) 0.455(2) 0.457(1)
scene 0.712(5) 0.728(3) 0.707(6) 0.634(7) 0.051(8) 0.734(2) 0.718(4) 0.752(1)
emotions 0.635(5) 0.637(4) 0.631(6) 0.581(7) 0.046(8) 0.667(1) 0.521(3) 0.651(2)
music 0.705(2) 0.615(7) 0.695(4) 0.689(5) 0.455(8) 0.654(6) 0.702(3) 0.750(1)
corel5k 0.274(4) 0.267(5) 0.260(6) 0.279(3) 0.294(2) 0.112(8) 0.295(1) 0.250(7)
enron 0.219(4) 0.225(2) 0.187(7) 0.168(8) 0.214(5) 0.212(6) 0.220(3) 0.235(1)
bibtex 0.143(7) 0.114(8) 0.180(4) 0.175(5) 0.198(3) 0.210(2) 0.174(6) 0.320(1)
flags 0.654(4) 0.674(2) 0.582(7) 0.652(5) 0.562(8) 0.638(6) 0.667(3) 0.750(1)
birds 0.301(4) 0.324(3) 0.218(7) 0.275(5) 0.388(2) 0.059(8) 0.274(6) 0.451(1)
genbase 0.551(2) 0.498(5) 0.540(3) 0.427(7) 0.414(8) 0.438(6) 0.501(4) 0.582(1)
Average score 0.4579(2) 0.448(4) 0.4374(5) 0.4263(6) 0.2977 (8) 0.4125(7) 0.4527(3) 0.5198(1)
Table 5. Macro F1-score of different multi-label classification algorithms ↑
Dataset EBR ECC EPS RAkEL Adaboost.MH MLkNN MLS EMSTE
yeast 0.385(6) 0.398(5) 0.374(8) 0.383(7) 0.405(3) 0.401(4) 0.431(2) 0.455(1)
scene 0.712(4) 0.728(3) 0.707(5) 0.634(6) 0.001(8) 0.734(2) 0.621(7) 0.754(1)
emotions 0.635(5) 0.637(4) 0.631(6) 0.581(7) 0.046(8) 0.667 (1) 0.651(3) 0.652(2)
music 0.705(2) 0.615(6) 0.695(3) 0.689(4) 0.455(8) 0.654(7) 0.686(5) 0.751(1)
corel5k 0.274(3) 0.267(4) 0.260(5) 0.279(2) 0.294(1) 0.112(7) 0.401(3) 0.251(6)
enron 0.219(4) 0.225(2) 0.187(7) 0.168(8) 0.214(5) 0.212(6) 0.222(3) 0.245(1)
bibtex 0.143(7) 0.114(8) 0.180(5) 0.175(6) 0.198(4) 0.210(3) 0.311(2) 0.321(1)
flags 0.652(3) 0.674(2) 0.582(7) 0.650(5) 0.562(8) 0.638(6) 0.655(4) 0.751(1)
birds 0.301(4) 0.388(1) 0.218(6) 0.275(5) 0.358(3) 0.059(7) 0.401(3) 0.361(2)
genbase 0.551(3) 0.498(5) 0.540(4) 0.427(7) 0.414(8) 0.438(6) 0.570(2) 0.581(1)
Average score 0.4577(3) 0.4544(4) 0.4374(5) 0.4261(6) 0.2947(8) 0.4125(7) 0.4949(2) 0.5122(1)
Table 6. Micro F1-score of different multi-label classification algorithms ↑
Dataset EBR ECC EPS RAkEL Adaboost.MH MLkNN MLS EMSTE
yeast 0.554(3) 0.532(7) 0.545(5) 0.550(4) 0.559(2) 0.528(8) 0.534(6) 0.564(1)
scene 0.708(7) 0.724(2) 0.722(4) 0.711(6) 0.02(8) 0.712(5) 0.723(3) 0.764(1)
emotions 0.665(3) 0.656(4) 0.671(2) 0.655(5) 0.646(7) 0.638(8) 0.650(6) 0.681(1)
music 0.709(2) 0.615(7) 0.595(8) 0.689(3) 0.668(5) 0.663(6) 0.670(4) 0.788(1)
corel5k 0.390(1) 0.284(8) 0.290(7) 0.320(6) 0.339(5) 0.359(4) 0.361(3) 0.383(2)
enron 0.563(4) 0.584(1) 0.489(7) 0.580(2) 0.561(5) 0.538(6) 0.401(3) 0. 573(3)
bibtex 0.343(6) 0.234(7) 0.432(5) 0.443(4) 0.467(3) 0.232(8) 0.534(2) 0.543(1)
flags 0.622(6) 0.734(2) 0.542(8) 0.562(7) 0.731(3) 0.754(1) 0.688(5) 0.698(4)
birds 0.441(4) 0.424(6) 0.407(7) 0.475(3) 0.488(2) 0.438(5) 0.401(3) 0.491(1)
genbase 0.347(8) 0.387(5) 0.378(6) 0.356(7) 0.456(3) 0.476(2) 0.431(4) 0.565(1)
Average score 0.5342(3) 0.5174(6) 0.5071(7) 0.5341(4) 0.4935(8) 0.5338(5) 0.5393(2) 0.605(1)
Multi-label learning by extended multi-tier stacked ensemble method with label correlated ... (Hemavati)
11. 3394 ❒ ISSN: 2088-8708
Table 7. Hamming loss of different multi-label classification algorithms ↓
Dataset EBR ECC EPS RAkEL Adaboost.MH MLkNN MLS EMSTE
yeast 0.206(2) 0.210(4) 0.212(5) 0.247(8) 0.232(6) 0.209(3) 0.238(7) 0.202(1)
scene 0.0942(1) 0.098(4) 0.097(3) 0.138(6) 0.178(8) 0.099(5) 0.143(7) 0.095(2)
emotions 0.197(2) 0.205(3) 0.212(4) 0.265(7) 0.306(8) 0.235(6) 0.254(5) 0.192(1)
music 0.025(2) 0.021(1) 0.125(6) 0.109(5) 0.212(7) 0.238(8) 0.060(4) 0.055(3)
corel5k 0.174(3) 0.167(2) 0.160(1) 0.279(7) 0.294(8) 0.238(4) 0.255(6) 0.251(5)
enron 0.163(3) 0.187(6) 0.170(5) 0.168(4) 0.191(7) 0.218(8) 0.092(2) 0.051(1)
bibtex 0.249(4) 0.245(3) 0.259(6) 0.253(5) 0.278(8) 0.232(1) 0.277(7) 0.236(2)
flags 0.227(5) 0.297(8) 0.098(2) 0.162(4) 0.121(3) 0.228(6) 0.252(7) 0.031(1)
birds 0.043(2) 0.045(3) 0.046(4) 0.075(7) 0.052(5) 0.0.054(6) 0.151(8) 0.041(1)
genbase 0.022(1) 0.102(2) 0.140(3) 0.221(6) 0.314(8) 0.238(7) 0.211(5) 0.151(4)
Average score 0.14002(2) 0.1577(4) 0.1519(3) 0.1917 (5) 0.2178(7) 0.2189 (8) 0.1933(6) 0.1305(1)
(a) (b)
(c) (d)
(e)
Figure 2. Critical distance diagrams for the comparison algorithms of different performance evaluation
metrics (a) average precision, (b) F1, (c) macro F1, (d) micro F1, and (e) Hamming loss
In each CD diagram of Figure 2 any comparison algorithm connected with EMSTE are having a
different performance among them significantly. EBR performs well on Hamming loss (HLoss) , because of
its first-order label correlation approach that tries to optimize the Hamming loss. We can observe EMSTE
outperforms in terms of HLoss. We have used ECC as a comparison algorithm to show how the high order
label correlation approach outperforms the other approaches on MicroF1, which considers the global label
correlations. But the proposed EMSTE outperforms in terms of these methods. Figure 3 shows the graph
that compares the average score of performance evaluation metrics Average Precision and F1 Score of different
comparison algorithms and Figure 4 shows the graph that compares the average score of performance evaluation
metrics macro F1 and micro F1 Score and Hamming loss of different comparison algorithms. It is clear from the
graph that the performance of the proposed method i.e., EMSTE is better in every case. EMSTE outperforms
Int J Elec & Comp Eng, Vol. 13, No. 3, June 2023: 3384-3397
12. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 3395
compared to other methods concerning other evaluation metrics. This indicates the superiority of the proposed
method and the effectiveness of augmentation of correlated features in meta-learning.
Figure 3. Average score of performance evaluation metrics F1 score
Figure 4. Average score of performance evaluation metrics macro F1, micro F1, Haming loss
7. CONCLUSION
In this paper, we have experimented with the novel extended multi-tier stacked ensemble method
(EMSTE) for multi-label classification to enhance the performance of the classification by considering label
correlated feature subset selection and by augmenting those features while constructing the meta data set at
the generalization tier. In this approach, we are trying to control the bias and the variance at the ensemble tier
by making use of combination schemes and improving the performance of classification at the generalization
tier. We have tested our method with ten datasets and compared it with a number of different multi-label
classification algorithms and our method is giving very good results.
REFERENCES
[1] I. Katakis, G. Tsoumakas, and I. Vlahavas, “Multilabel text classification for automated tag suggestion,” in Proceed-
ings of the ECMLPKDD 2008 Discovery Challenge, vol. 9, no. 3, pp. 1–9, 2008.
[2] H. Weng et al., “Multi-label symptom analysis and modeling of TCM diagnosis of hypertension,” in Pro-
ceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, pp. 1922–1929, 2019,
doi: 10.1109/BIBM.2018.8621173.
[3] R. E. Schapire and Y. Singer, “BoosTexter: a boosting-based system for text categorization,” Machine Learning,
vol. 39, no. 2, pp. 135–168, 2000, doi: 10.1023/a:1007649029923.
[4] R. B. Mangolin et al., “A multimodal approach for multi-label movie genre classification,” Multimedia Tools and
Applications, vol. 81, no. 14, pp. 19071–19096, Jun. 2022, doi: 10.1007/s11042-020-10086-2.
[5] Y. Zhang and D. Y. Yeung, “Multilabel relationship learning,” ACM Transactions on Knowledge Discovery from Data,
vol. 7, no. 2, 2013, doi: 10.1145/2499907.2499910.
Multi-label learning by extended multi-tier stacked ensemble method with label correlated ... (Hemavati)
13. 3396 ❒ ISSN: 2088-8708
[6] M. L. Zhang and Z. H. Zhou, “A review on multi-label learning algorithms,” IEEE Transactions on Knowledge and
Data Engineering, vol. 26, no. 8, pp. 1819–1837, 2014, doi: 10.1109/TKDE.2013.39.
[7] G. Nasierding and A. Z. Kouzani, “Comparative evaluation of multi-label classification methods,”
in International Conference on Fuzzy Systems and Knowledge Discovery, May 2012, pp. 679–683,
doi: 10.1109/FSKD.2012.6234347.
[8] L. Rokach, A. Schclar, and E. Itach, “Ensemble methods for multi-label classification,” Expert Systems with Applica-
tions, vol. 41, no. 16, pp. 7507–7523, Jul. 2013, doi: 10.1016/j.eswa.2014.06.015.
[9] M. R. Boutell, J. Luo, X. Shen, and C. M. Brown, “Learning multi-label scene classification,” Pattern Recognition,
vol. 37, no. 9, pp. 1757–1771, 2004, doi: 10.1016/j.patcog.2004.03.009.
[10] J. Read, “A pruned problem transformation method for multi-label classification,” in New Zealand Computer Science
Research Student Conference, 2008, pp. 143–150.
[11] G. Tsoumakas, I. Katakis, and I. Vlahavas, “Random k-labelsets for multilabel classification,” IEEE Transactions on
Knowledge and Data Engineering, vol. 23, no. 7, pp. 1079–1089, Jul. 2011, doi: 10.1109/TKDE.2010.164.
[12] G. Chen, Y. Song, F. Wang, and C. Zhang, “Semi-supervised multi-label learning by solving a sylvester equa-
tion,” in Proceedings of the 2008 SIAM International Conference on Data Mining, Apr. 2008, vol. 1, pp. 410–419,
doi: 10.1137/1.9781611972788.37.
[13] J. Read, B. Pfahringer, and G. Holmes, “Multi-label classification using ensembles of pruned sets,” in Proceedings -
IEEE International Conference on Data Mining, ICDM, pp. 995–1000, 2008, doi: 10.1109/ICDM.2008.74.
[14] M.-L. Zhang and Z.-H. Zhou, “ML-kNN: a lazy learning approach to multi-label learning,” Pattern Recognition,
vol. 40, no. 7, pp. 2038–2048, Jul. 2007, doi: 10.1016/j.patcog.2006.12.019.
[15] G. Tsoumakas, I. Katakis, and I. Vlahavas, “Mining multi-label data,” in Data Mining and Knowledge Discovery
Handbook, Boston, MA: Springer US, 2009, pp. 667–685.
[16] E. Hüllermeier, J. Fürnkranz, W. Cheng, and K. Brinker, “Label ranking by learning pairwise preferences,” Artificial
Intelligence, vol. 172, no. 16–17, pp. 1897–1916, 2008, doi: 10.1016/j.artint.2008.08.002.
[17] J. Fürnkranz, E. Hüllermeier, E. Loza Mencı́a, and K. Brinker, “Multilabel classification via calibrated label ranking,”
Machine Learning, vol. 73, no. 2, pp. 133–153, Nov. 2008, doi: 10.1007/s10994-008-5064-8.
[18] A. Elisseeff and J. Weston, “A kernel method for multi-labelled classification,” in Proceedings of the 14th
International
Conference on Neural Information Processing Systems: Natural and Synthetic, 2001, pp. 681–687.
[19] Z.-F. He, M. Yang, Y. Gao, H.-D. Liu, and Y. Yin, “Joint multi-label classification and label correlations
with missing labels and feature selection,” Knowledge-Based Systems, vol. 163, pp. 145–158, Jan. 2019,
doi: 10.1016/j.knosys.2018.08.018.
[20] Z. Abdallah, A. El-Zaart, and M. Oueidat, “An improvement of label power set method based on priority label
transformation,” International Journal of Applied Engineering Research, vol. 11, no. 16, pp. 9079–9087, 2016.
[21] X. Geng, “Label distribution learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 7,
pp. 1734–1748, 2016, doi: 10.1109/TKDE.2016.2545658.
[22] X. Zhou, Z. Wei, M. Xu, S. Qu, and G. Guo, “Facial depression recognition by deep joint label distribution
and metric learning,” IEEE Transactions on Affective Computing, vol. 13, no. 3, pp. 1605–1618, Jul. 2022,
doi: 10.1109/TAFFC.2020.3022732.
[23] X. Wen et al., “Adaptive variance based label distribution learning for facial age estimation,” in Lecture Notes in
Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
vol. 12368, 2020, pp. 379–395.
[24] Y. Xia, K. Chen, and Y. Yang, “Multi-label classification with weighted classifier selection and stacked ensemble,”
Information Sciences, vol. 557, pp. 421–442, May 2021, doi: 10.1016/j.ins.2020.06.017.
[25] J. Lee and D.-W. Kim, “Mutual Information-based multi-label feature selection using interaction information,” Expert
Systems with Applications, vol. 42, no. 4, pp. 2013–2025, Mar. 2015, doi: 10.1016/j.eswa.2014.09.063.
[26] J. Lee and D. W. Kim, “SCLS: Multi-label feature selection based on scalable criterion for large label set,” Pattern
Recognition, vol. 66, pp. 342–352, 2017, doi: 10.1016/j.patcog.2017.01.014.
[27] Y. Lin, Q. Hu, J. Liu, and J. Duan, “Multi-label feature selection based on max-dependency and min-redundancy,”
Neurocomputing, vol. 168, pp. 92–103, 2015, doi: 10.1016/j.neucom.2015.06.010.
[28] P. Zhang, W. Gao, J. Hu, and Y. Li, “Multi-label feature selection based on high-order label correlation assumption,”
Entropy, vol. 22, no. 7, Jul. 2020, doi: 10.3390/e22070797.
[29] M. Sorower, “A literature survey on algorithms for multi-label learning,” Oregon State University, Corvallis.
pp. 1–25, 2010.
[30] “Mulan: a java library for multi-label learning.” sourceforge, https://mulan.sourceforge.net/datasets-mlc.html
(accessed Dec. 1, 2022).
[31] Z. F. He, M. Yang, and H. D. Liu, “Joint learning of multi-label classification and label correlations,” Ruan Jian Xue
Bao/Journal of Software, vol. 25, no. 9, pp. 1967–1981, 2014, doi: 10.13328/j.cnki.jos.004634.
[32] J. M. Moyano, E. L. Gibaja, K. J. Cios, and S. Ventura, “Review of ensembles of multi-label clas-
sifiers: Models, experimental study and prospects,” Information Fusion, vol. 44, pp. 33–45, Nov. 2018,
Int J Elec & Comp Eng, Vol. 13, No. 3, June 2023: 3384-3397
14. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 3397
doi: 10.1016/j.inffus.2017.12.001.
[33] J. Read, B. Pfahringer, G. Holmes, and E. Frank, “Classifier chains for multi-label classification,” in Lecture Notes in
Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
vol. 5782, no. 2, 2009, pp. 254–269.
[34] M.-L. Zhang and K. Zhang, “Multi-label learning by exploiting label dependency,” in Proceedings of
the 16th
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010,
doi: 10.1145/1835804.1835930.
[35] E. Spyromitros, G. Tsoumakas, and I. Vlahavas, “An empirical study of lazy multilabel classification algorithms,”
in Artificial Intelligence: Theories, Models and Applications, Berlin, Heidelberg: Springer Berlin Heidelberg,
pp. 401–406, 2008.
[36] “MEKA,” GitHub. https://waikato.github.io/meka/ (accessed Jan. 1, 2022).
[37] E. A. Tanaka, S. R. Nozawa, A. A. Macedo, and J. A. Baranauskas, “A multi-label approach using binary relevance
and decision trees applied to functional genomics,” Journal of Biomedical Informatics, vol. 54, pp. 85–95, Apr. 2015,
doi: 10.1016/j.jbi.2014.12.011.
[38] Z. Wei, H. Zhang, Z. Zhang, W. Li, and D. Miao, “A naive Bayesian multi-label classification algorithm with appli-
cation to visualize text search results,” International Journal of Advanced Intelligence, vol. 3, no. 2, pp. 173–188,
2011.
[39] A. Patle and D. S. Chouhan, “SVM kernel functions for classification,” in 2013 International Conference on Advances
in Technology and Engineering, 2013, doi: 10.1109/ICAdTE.2013.6524743.
[40] J. Huang, G. Li, Q. Huang, and X. Wu, “Learning label specific features for multi-label classification,” in 2015 IEEE
International Conference on Data Mining, Nov. 2015, pp. 181–190, doi: 10.1109/ICDM.2015.67.
[41] A. Pakrashi and B. M. Namee, “Stacked-MLkNN: a stacking based improvement to multi-label k-nearest neighbours,”
in Proceedings of Machine Learning Research, 2017, pp. 51–63.
[42] M. Friedman, “A comparison of alternative tests of significance for the problem of m rankings,” The Annals of
Mathematical Statistics, vol. 11, no. 1, pp. 86–92, Mar. 1940, doi: 10.1214/aoms/1177731944.
[43] J. Demsar, “Statistical comparisons of classifiers over multiple data sets,” Journal of Machine Learning Research,
vol. 7, pp. 1–30, 2006.
BIOGRAPHIES OF AUTHORS
Hemavati received her Bachelor of Engineering degree in Information Science and En-
gineering, and M.Tech in Computer Science and Engineering from Siddaganga Institute of Technol-
ogy, Tumakuru, VTU University, Belgaum, India. She is currently a research scholar carrying out
her research work in PAMI Lab, IISc Bangalore, Karnataka, India. She is an Assistant Professor
at Siddaganga Institute of Technology, Tumakuru, India. Her areas of interests are AI and machine
learning. She can be contacted at email: hemavd@sit.ac.in.
Visweswariah Susheela Devi is a Principal Research Scientist in the Department of
Computer Science and Automation, Indian Institute of Science. She received her Ph.D. from In-
dian Institute of Science, Bangalore. Her research topics include pattern recognition, data mining,
soft computing, machine learning, Deep learning. She wrote four Books, many book chapters, and
published many papers. She is a reviewer for many conferences and journal papers including IEEE.
She was session chair for many conferences. She supervises M.Tech., M.Tech. research and Ph.D.
Students. She has taught many courses like soft computing, intelligent agents, and machine learning.
She has delivered many invited lecture talks. She can be contacted at email: susheela@iisc.ac.in.
Ramalingappa Aparna received her M.S. from BITS Pillani, and Ph.D. from VTU, Bel-
gaum. She has been working as Professor and Head of the department, at Siddaganga Institute of
Technology Tumakuru. Her main research interests include cryptography and network security, se-
curity in wireless sensor networks, routing issues in ad hoc wireless networks, and machine learning.
She has taught many subjects for undergraduate and post graduate students, and she is supervising
M.Tech. and Ph.D. students. She can be contacted at email: raparna@sit.ac.in.
Multi-label learning by extended multi-tier stacked ensemble method with label correlated ... (Hemavati)