This document summarizes a research paper on feature selection algorithms for supervised and semi-supervised clustering. It discusses how semi-supervised learning uses both labeled and unlabeled data for training, between unsupervised and supervised learning. It also describes a fast clustering-based feature selection algorithm (FAST) that works in two steps: 1) using graph-theoretic clustering to separate features into clusters, and 2) selecting the most representative feature from each cluster to form a subset of features. The algorithm aims to efficiently obtain a good feature subset by removing unrelated and redundant features.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
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Iaetsd an enhanced feature selection forIaetsd Iaetsd
The document discusses feature selection techniques for machine learning applications. It proposes an Enhanced Fast Clustering-based Feature Selection (EFAST) algorithm. The EFAST algorithm works in two steps: 1) features are clustered using graph-theoretic clustering methods, and 2) the most relevant representative feature strongly correlated with the target categories is selected from each cluster to form the optimal feature subset. Features from different clusters are relatively independent, so EFAST has a high chance of selecting a set of useful and independent features. The algorithm was tested on real-world data and showed improved performance over other feature selection methods by reducing features while also improving classifier performance.
High dimesional data (FAST clustering ALG) PPTdeepan v
The document presents a feature selection algorithm called FAST (Fast clustering-based feature selection algorithm). FAST uses minimum spanning trees and clustering to identify relevant feature subsets while removing irrelevant and redundant features. This achieves dimensionality reduction and improves the accuracy of learning algorithms. The algorithm was experimentally evaluated on datasets with over 10,000 features and was shown to outperform other feature selection methods in terms of time complexity and selected feature proportions.
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
DOTNET 2013 IEEE CLOUDCOMPUTING PROJECT A fast clustering based feature subse...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
A fast clustering based feature subset selection algorithm for high-dimension...JPINFOTECH JAYAPRAKASH
The document proposes a fast clustering-based feature selection algorithm (FAST) to efficiently and effectively select useful feature subsets from high-dimensional data. FAST works in two steps: (1) it clusters features using minimum spanning trees, partitioning clusters so each represents a subset of independent features; (2) it selects the most representative feature from each cluster to form the output subset. Experiments on 35 real-world datasets show FAST not only selects smaller feature subsets but also improves performance of four common classifiers compared to other feature selection methods.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Iaetsd an enhanced feature selection forIaetsd Iaetsd
The document discusses feature selection techniques for machine learning applications. It proposes an Enhanced Fast Clustering-based Feature Selection (EFAST) algorithm. The EFAST algorithm works in two steps: 1) features are clustered using graph-theoretic clustering methods, and 2) the most relevant representative feature strongly correlated with the target categories is selected from each cluster to form the optimal feature subset. Features from different clusters are relatively independent, so EFAST has a high chance of selecting a set of useful and independent features. The algorithm was tested on real-world data and showed improved performance over other feature selection methods by reducing features while also improving classifier performance.
High dimesional data (FAST clustering ALG) PPTdeepan v
The document presents a feature selection algorithm called FAST (Fast clustering-based feature selection algorithm). FAST uses minimum spanning trees and clustering to identify relevant feature subsets while removing irrelevant and redundant features. This achieves dimensionality reduction and improves the accuracy of learning algorithms. The algorithm was experimentally evaluated on datasets with over 10,000 features and was shown to outperform other feature selection methods in terms of time complexity and selected feature proportions.
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
DOTNET 2013 IEEE CLOUDCOMPUTING PROJECT A fast clustering based feature subse...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
A fast clustering based feature subset selection algorithm for high-dimension...JPINFOTECH JAYAPRAKASH
The document proposes a fast clustering-based feature selection algorithm (FAST) to efficiently and effectively select useful feature subsets from high-dimensional data. FAST works in two steps: (1) it clusters features using minimum spanning trees, partitioning clusters so each represents a subset of independent features; (2) it selects the most representative feature from each cluster to form the output subset. Experiments on 35 real-world datasets show FAST not only selects smaller feature subsets but also improves performance of four common classifiers compared to other feature selection methods.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
EFFICIENT FEATURE SUBSET SELECTION MODEL FOR HIGH DIMENSIONAL DATAIJCI JOURNAL
This paper proposes a new method that intends on reducing the size of high dimensional dataset by
identifying and removing irrelevant and redundant features. Dataset reduction is important in the case of
machine learning and data mining. The measure of dependence is used to evaluate the relationship
between feature and target concept and or between features for irrelevant and redundant feature removal.
The proposed work initially removes all the irrelevant features and then a minimum spanning tree of
relevant features is constructed using Prim’s algorithm. Splitting the minimum spanning tree based on the
dependency between features leads to the generation of forests. A representative feature from each of the
forests is taken to form the final feature subset
Iaetsd an efficient and large data base using subset selection algorithmIaetsd Iaetsd
The document presents a new feature selection algorithm called FAST (Feature Cluster-based Subset Selection) that aims to efficiently reduce dimensionality by removing irrelevant and redundant features. The FAST algorithm works in two steps: (1) it clusters features using graph theoretic methods, and (2) it selects the most representative feature from each cluster. This clustering-based approach has a high probability of selecting useful and independent features. The algorithm is evaluated on high dimensional datasets and shown to improve learning accuracy while reducing dimensionality compared to other feature selection methods.
C LUSTERING B ASED A TTRIBUTE S UBSET S ELECTION U SING F AST A LGORITHmIJCI JOURNAL
In machine learning and data mining, attribute sel
ect is the practice of selecting a subset o
f most
consequential attributes for utilize in model const
ruction. Using an attribute select method is that t
he data
encloses many redundant or extraneous attributes. W
here redundant attributes are those which sup
ply
no supplemental information than the presently
selected attributes, and impertinent attribut
es offer
no valuable information in any context
A Survey on Constellation Based Attribute Selection Method for High Dimension...IJERA Editor
Attribute Selection is an important topic in Data Mining, because it is the effective way for reducing dimensionality, removing irrelevant data, removing redundant data, & increasing accuracy of the data. It is the process of identifying a subset of the most useful attributes that produces compatible results as the original entire set of attribute. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group called a cluster are more similar in some sense or another to each other than to those in other groups (Clusters). There are various approaches & techniques for attribute subset selection namely Wrapper approach, Filter Approach, Relief Algorithm, Distributional clustering etc. But each of one having some disadvantages like unable to handle large volumes of data, computational complexity, accuracy is not guaranteed, difficult to evaluate and redundancy detection etc. To get the upper hand on some of these issues in attribute selection this paper proposes a technique that aims to design an effective clustering based attribute selection method for high dimensional data. Initially, attributes are divided into clusters by using graph-based clustering method like minimum spanning tree (MST). In the second step, the most representative attribute that is strongly related to target classes is selected from each cluster to form a subset of attributes. The purpose is to increase the level of accuracy, reduce dimensionality; shorter training time and improves generalization by reducing over fitting.
Classification problems specified in high dimensional data with smallnumber of observation are generally becoming common in specific microarray data. In the time of last two periods of years, manyefficient classification standard models and also Feature Selection (FS) algorithm which isalso referred as FS technique have basically been proposed for higher prediction accuracies. Although, the outcome of FS algorithm related to predicting accuracy is going to be unstable over the variations in considered trainingset, in high dimensional data. In this paperwe present a latest evaluation measure Q-statistic that includes the stability of the selected feature subset in inclusion to prediction accuracy. Then we are going to propose the standard Booster of a FS algorithm that boosts the basic value of the preferred Q-statistic of the algorithm applied. Therefore study on synthetic data and 14 microarray data sets shows that Booster boosts not only the value of Q-statistics but also the prediction accuracy of the algorithm applied.
Survey on Supervised Method for Face Image Retrieval Based on Euclidean Dist...Editor IJCATR
This document summarizes various supervised methods for face image retrieval based on Euclidean distance. It discusses literature on active shape models, principal component analysis, linear discriminant analysis, locality-constrained linear coding, bag-of-words models, local binary patterns, and support vector machines. It evaluates support vector machines as the best classifier for face image retrieval systems due to its ability to significantly reduce the need for labeled training data and accurately classify faces, proteins, and characters. The document concludes that a content-based face retrieval system using support vector machines improves detection performance by retrieving similar faces from a database based on Euclidean distance calculations between local binary pattern features of the query and database images.
Effective Feature Selection for Feature Possessing Group Structurerahulmonikasharma
This document proposes a new method called efficient group variable selection (EGVS) for feature selection when features have a group structure. EGVS has two stages: 1) within-group variable selection evaluates each feature individually to select discriminative features within each group. 2) Between-group variable selection re-evaluates all features to remove redundancy and obtain an optimal subset by considering relationships between groups. The method is demonstrated on benchmark datasets, showing it increases classification accuracy by leveraging the group structure during feature selection.
Hybridization of Meta-heuristics for Optimizing Routing protocol in VANETsIJERA Editor
The goal of VANET is to establish a vehicular communication system which is reliable and fast which caters to
road safety and road safety. In VANET where network fragmentation is frequent with no central control, routing
becomes a challenging task. Planning an optimal routing plan for tuning parameter configuration of routing
protocol for setting up VANET is very crucial. This is done by defining an optimization problem where
hybridization of meta-heuristics is defined. The paper contributes the idea of combining meta-heuristic
algorithm to enhance the performance of individual search method for optimization problem.
Feature Selection : A Novel Approach for the Prediction of Learning Disabilit...csandit
Feature selection is a problem closely related to dimensionality reduction. A commonly used
approach in feature selection is ranking the individual features according to some criteria and
then search for an optimal feature subset based on an evaluation criterion to test the optimality.
The objective of this work is to predict more accurately the presence of Learning Disability
(LD) in school-aged children with reduced number of symptoms. For this purpose, a novel
hybrid feature selection approach is proposed by integrating a popular Rough Set based feature
ranking process with a modified backward feature elimination algorithm. The approach follows
a ranking of the symptoms of LD according to their importance in the data domain. Each
symptoms significance or priority values reflect its relative importance to predict LD among the
various cases. Then by eliminating least significant features one by one and evaluating the
feature subset at each stage of the process, an optimal feature subset is generated. The
experimental results shows the success of the proposed method in removing redundant
attributes efficiently from the LD dataset without sacrificing the classification performance.
I gave this talk in the EDBT 2014 conference, which tool place in Athens, Greece.
I show how data examples can be used to characterize the behavior of scientific modules. I present a new methods that automatically generate the data examples, and show that such data examples are useful for the human user to understand the task of the modules, and that they can be used to assist curators in repairing broken workflows (i.e., workflows for which one or more modules are no longer supplied by their providers)
Network Based Intrusion Detection System using Filter Based Feature Selection...IRJET Journal
This document proposes a mutual information-based feature selection algorithm to select optimal features for network intrusion detection classification. The algorithm aims to handle dependent data features better than previous methods. It evaluates the effectiveness of the algorithm on network intrusion detection cases. Most previous methods suffer from low detection rates and high false alarm rates. The proposed approach uses feature selection, filtering, clustering, and clustering ensemble techniques in a hybrid data mining method to achieve high accuracy for intrusion detection systems.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
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 ROBUST MISSING VALUE IMPUTATION METHOD MIFOIMPUTE FOR INCOMPLETE MOLECULAR ...ijcsa
This document presents a new method called MiFoImpute for imputing missing values in molecular descriptor datasets. MiFoImpute uses an iterative random forest approach. It is compared to 10 other imputation methods on two molecular descriptor datasets with varying percentages of artificially introduced missing values (10-30%). Experimental results show that MiFoImpute has competitive or better performance than other methods according to NRMSE and NMAE error metrics. It exhibits robustness to increasing levels of missing data and computational efficiency compared to some other methods.
Semi-supervised learning approach using modified self-training algorithm to c...IJECEIAES
Burst header packet flooding is an attack on optical burst switching (OBS) network which may cause denial of service. Application of machine learning technique to detect malicious nodes in OBS network is relatively new. As finding sufficient amount of labeled data to perform supervised learning is difficult, semi-supervised method of learning (SSML) can be leveraged. In this paper, we studied the classical self-training algorithm (ST) which uses SSML paradigm. Generally, in ST, the available true-labeled data (L) is used to train a base classifier. Then it predicts the labels of unlabeled data (U). A portion from the newly labeled data is removed from U based on prediction confidence and combined with L. The resulting data is then used to re-train the classifier. This process is repeated until convergence. This paper proposes a modified self-training method (MST). We trained multiple classifiers on L in two stages and leveraged agreement among those classifiers to determine labels. The performance of MST was compared with ST on several datasets and significant improvement was found. We applied the MST on a simulated OBS network dataset and found very high accuracy with a small number of labeled data. Finally we compared this work with some related works.
IRJET- Classifying Twitter Data in Multiple Classes based on Sentiment Class ...IRJET Journal
This document presents a proposed model for classifying Twitter data into multiple sentiment classes using machine learning techniques. The model first preprocesses the Twitter data by removing stop words and special characters. It then applies a negation filter to group the data into positive and negative classes based on the presence of negation words. Natural language processing is used to extract part-of-speech features from the text, transforming it into a structured format. The support vector machine classifier is trained on the labeled data and used to predict the sentiment class of new text data. The model's performance is evaluated based on accuracy, error rate, memory usage, and time consumption, demonstrating that it can accurately classify Twitter data into multiple sentiment classes.
The document describes a proposed fast clustering-based feature subset selection (FAST) algorithm for high-dimensional data. The FAST algorithm works in two steps: 1) clustering features using minimum spanning tree methods, and 2) selecting the most representative feature from each cluster. This identifies useful and independent features efficiently. Experimental results on 35 real-world datasets demonstrate that FAST produces smaller feature subsets and improves classifier performance compared to other feature selection algorithms.
JAVA 2013 IEEE DATAMINING PROJECT A fast clustering based feature subset sele...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
JAVA 2013 IEEE PROJECT A fast clustering based feature subset selection algor...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
JAVA 2013 IEEE CLOUDCOMPUTING PROJECT A fast clustering based feature subset ...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Unsupervised Feature Selection Based on the Distribution of Features Attribut...Waqas Tariq
Since dealing with high dimensional data is computationally complex and sometimes even intractable, recently several feature reductions methods have been developed to reduce the dimensionality of the data in order to simplify the calculation analysis in various applications such as text categorization, signal processing, image retrieval, gene expressions and etc. Among feature reduction techniques, feature selection is one the most popular methods due to the preservation of the original features. However, most of the current feature selection methods do not have a good performance when fed on imbalanced data sets which are pervasive in real world applications. In this paper, we propose a new unsupervised feature selection method attributed to imbalanced data sets, which will remove redundant features from the original feature space based on the distribution of features. To show the effectiveness of the proposed method, popular feature selection methods have been implemented and compared. Experimental results on the several imbalanced data sets, derived from UCI repository database, illustrate the effectiveness of our proposed methods in comparison with the other compared methods in terms of both accuracy and the number of selected features.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
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
EFFICIENT FEATURE SUBSET SELECTION MODEL FOR HIGH DIMENSIONAL DATAIJCI JOURNAL
This paper proposes a new method that intends on reducing the size of high dimensional dataset by
identifying and removing irrelevant and redundant features. Dataset reduction is important in the case of
machine learning and data mining. The measure of dependence is used to evaluate the relationship
between feature and target concept and or between features for irrelevant and redundant feature removal.
The proposed work initially removes all the irrelevant features and then a minimum spanning tree of
relevant features is constructed using Prim’s algorithm. Splitting the minimum spanning tree based on the
dependency between features leads to the generation of forests. A representative feature from each of the
forests is taken to form the final feature subset
Iaetsd an efficient and large data base using subset selection algorithmIaetsd Iaetsd
The document presents a new feature selection algorithm called FAST (Feature Cluster-based Subset Selection) that aims to efficiently reduce dimensionality by removing irrelevant and redundant features. The FAST algorithm works in two steps: (1) it clusters features using graph theoretic methods, and (2) it selects the most representative feature from each cluster. This clustering-based approach has a high probability of selecting useful and independent features. The algorithm is evaluated on high dimensional datasets and shown to improve learning accuracy while reducing dimensionality compared to other feature selection methods.
C LUSTERING B ASED A TTRIBUTE S UBSET S ELECTION U SING F AST A LGORITHmIJCI JOURNAL
In machine learning and data mining, attribute sel
ect is the practice of selecting a subset o
f most
consequential attributes for utilize in model const
ruction. Using an attribute select method is that t
he data
encloses many redundant or extraneous attributes. W
here redundant attributes are those which sup
ply
no supplemental information than the presently
selected attributes, and impertinent attribut
es offer
no valuable information in any context
A Survey on Constellation Based Attribute Selection Method for High Dimension...IJERA Editor
Attribute Selection is an important topic in Data Mining, because it is the effective way for reducing dimensionality, removing irrelevant data, removing redundant data, & increasing accuracy of the data. It is the process of identifying a subset of the most useful attributes that produces compatible results as the original entire set of attribute. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group called a cluster are more similar in some sense or another to each other than to those in other groups (Clusters). There are various approaches & techniques for attribute subset selection namely Wrapper approach, Filter Approach, Relief Algorithm, Distributional clustering etc. But each of one having some disadvantages like unable to handle large volumes of data, computational complexity, accuracy is not guaranteed, difficult to evaluate and redundancy detection etc. To get the upper hand on some of these issues in attribute selection this paper proposes a technique that aims to design an effective clustering based attribute selection method for high dimensional data. Initially, attributes are divided into clusters by using graph-based clustering method like minimum spanning tree (MST). In the second step, the most representative attribute that is strongly related to target classes is selected from each cluster to form a subset of attributes. The purpose is to increase the level of accuracy, reduce dimensionality; shorter training time and improves generalization by reducing over fitting.
Classification problems specified in high dimensional data with smallnumber of observation are generally becoming common in specific microarray data. In the time of last two periods of years, manyefficient classification standard models and also Feature Selection (FS) algorithm which isalso referred as FS technique have basically been proposed for higher prediction accuracies. Although, the outcome of FS algorithm related to predicting accuracy is going to be unstable over the variations in considered trainingset, in high dimensional data. In this paperwe present a latest evaluation measure Q-statistic that includes the stability of the selected feature subset in inclusion to prediction accuracy. Then we are going to propose the standard Booster of a FS algorithm that boosts the basic value of the preferred Q-statistic of the algorithm applied. Therefore study on synthetic data and 14 microarray data sets shows that Booster boosts not only the value of Q-statistics but also the prediction accuracy of the algorithm applied.
Survey on Supervised Method for Face Image Retrieval Based on Euclidean Dist...Editor IJCATR
This document summarizes various supervised methods for face image retrieval based on Euclidean distance. It discusses literature on active shape models, principal component analysis, linear discriminant analysis, locality-constrained linear coding, bag-of-words models, local binary patterns, and support vector machines. It evaluates support vector machines as the best classifier for face image retrieval systems due to its ability to significantly reduce the need for labeled training data and accurately classify faces, proteins, and characters. The document concludes that a content-based face retrieval system using support vector machines improves detection performance by retrieving similar faces from a database based on Euclidean distance calculations between local binary pattern features of the query and database images.
Effective Feature Selection for Feature Possessing Group Structurerahulmonikasharma
This document proposes a new method called efficient group variable selection (EGVS) for feature selection when features have a group structure. EGVS has two stages: 1) within-group variable selection evaluates each feature individually to select discriminative features within each group. 2) Between-group variable selection re-evaluates all features to remove redundancy and obtain an optimal subset by considering relationships between groups. The method is demonstrated on benchmark datasets, showing it increases classification accuracy by leveraging the group structure during feature selection.
Hybridization of Meta-heuristics for Optimizing Routing protocol in VANETsIJERA Editor
The goal of VANET is to establish a vehicular communication system which is reliable and fast which caters to
road safety and road safety. In VANET where network fragmentation is frequent with no central control, routing
becomes a challenging task. Planning an optimal routing plan for tuning parameter configuration of routing
protocol for setting up VANET is very crucial. This is done by defining an optimization problem where
hybridization of meta-heuristics is defined. The paper contributes the idea of combining meta-heuristic
algorithm to enhance the performance of individual search method for optimization problem.
Feature Selection : A Novel Approach for the Prediction of Learning Disabilit...csandit
Feature selection is a problem closely related to dimensionality reduction. A commonly used
approach in feature selection is ranking the individual features according to some criteria and
then search for an optimal feature subset based on an evaluation criterion to test the optimality.
The objective of this work is to predict more accurately the presence of Learning Disability
(LD) in school-aged children with reduced number of symptoms. For this purpose, a novel
hybrid feature selection approach is proposed by integrating a popular Rough Set based feature
ranking process with a modified backward feature elimination algorithm. The approach follows
a ranking of the symptoms of LD according to their importance in the data domain. Each
symptoms significance or priority values reflect its relative importance to predict LD among the
various cases. Then by eliminating least significant features one by one and evaluating the
feature subset at each stage of the process, an optimal feature subset is generated. The
experimental results shows the success of the proposed method in removing redundant
attributes efficiently from the LD dataset without sacrificing the classification performance.
I gave this talk in the EDBT 2014 conference, which tool place in Athens, Greece.
I show how data examples can be used to characterize the behavior of scientific modules. I present a new methods that automatically generate the data examples, and show that such data examples are useful for the human user to understand the task of the modules, and that they can be used to assist curators in repairing broken workflows (i.e., workflows for which one or more modules are no longer supplied by their providers)
Network Based Intrusion Detection System using Filter Based Feature Selection...IRJET Journal
This document proposes a mutual information-based feature selection algorithm to select optimal features for network intrusion detection classification. The algorithm aims to handle dependent data features better than previous methods. It evaluates the effectiveness of the algorithm on network intrusion detection cases. Most previous methods suffer from low detection rates and high false alarm rates. The proposed approach uses feature selection, filtering, clustering, and clustering ensemble techniques in a hybrid data mining method to achieve high accuracy for intrusion detection systems.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
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 ROBUST MISSING VALUE IMPUTATION METHOD MIFOIMPUTE FOR INCOMPLETE MOLECULAR ...ijcsa
This document presents a new method called MiFoImpute for imputing missing values in molecular descriptor datasets. MiFoImpute uses an iterative random forest approach. It is compared to 10 other imputation methods on two molecular descriptor datasets with varying percentages of artificially introduced missing values (10-30%). Experimental results show that MiFoImpute has competitive or better performance than other methods according to NRMSE and NMAE error metrics. It exhibits robustness to increasing levels of missing data and computational efficiency compared to some other methods.
Semi-supervised learning approach using modified self-training algorithm to c...IJECEIAES
Burst header packet flooding is an attack on optical burst switching (OBS) network which may cause denial of service. Application of machine learning technique to detect malicious nodes in OBS network is relatively new. As finding sufficient amount of labeled data to perform supervised learning is difficult, semi-supervised method of learning (SSML) can be leveraged. In this paper, we studied the classical self-training algorithm (ST) which uses SSML paradigm. Generally, in ST, the available true-labeled data (L) is used to train a base classifier. Then it predicts the labels of unlabeled data (U). A portion from the newly labeled data is removed from U based on prediction confidence and combined with L. The resulting data is then used to re-train the classifier. This process is repeated until convergence. This paper proposes a modified self-training method (MST). We trained multiple classifiers on L in two stages and leveraged agreement among those classifiers to determine labels. The performance of MST was compared with ST on several datasets and significant improvement was found. We applied the MST on a simulated OBS network dataset and found very high accuracy with a small number of labeled data. Finally we compared this work with some related works.
IRJET- Classifying Twitter Data in Multiple Classes based on Sentiment Class ...IRJET Journal
This document presents a proposed model for classifying Twitter data into multiple sentiment classes using machine learning techniques. The model first preprocesses the Twitter data by removing stop words and special characters. It then applies a negation filter to group the data into positive and negative classes based on the presence of negation words. Natural language processing is used to extract part-of-speech features from the text, transforming it into a structured format. The support vector machine classifier is trained on the labeled data and used to predict the sentiment class of new text data. The model's performance is evaluated based on accuracy, error rate, memory usage, and time consumption, demonstrating that it can accurately classify Twitter data into multiple sentiment classes.
The document describes a proposed fast clustering-based feature subset selection (FAST) algorithm for high-dimensional data. The FAST algorithm works in two steps: 1) clustering features using minimum spanning tree methods, and 2) selecting the most representative feature from each cluster. This identifies useful and independent features efficiently. Experimental results on 35 real-world datasets demonstrate that FAST produces smaller feature subsets and improves classifier performance compared to other feature selection algorithms.
JAVA 2013 IEEE DATAMINING PROJECT A fast clustering based feature subset sele...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
JAVA 2013 IEEE PROJECT A fast clustering based feature subset selection algor...IEEEGLOBALSOFTTECHNOLOGIES
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JAVA 2013 IEEE CLOUDCOMPUTING PROJECT A fast clustering based feature subset ...IEEEGLOBALSOFTTECHNOLOGIES
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Unsupervised Feature Selection Based on the Distribution of Features Attribut...Waqas Tariq
Since dealing with high dimensional data is computationally complex and sometimes even intractable, recently several feature reductions methods have been developed to reduce the dimensionality of the data in order to simplify the calculation analysis in various applications such as text categorization, signal processing, image retrieval, gene expressions and etc. Among feature reduction techniques, feature selection is one the most popular methods due to the preservation of the original features. However, most of the current feature selection methods do not have a good performance when fed on imbalanced data sets which are pervasive in real world applications. In this paper, we propose a new unsupervised feature selection method attributed to imbalanced data sets, which will remove redundant features from the original feature space based on the distribution of features. To show the effectiveness of the proposed method, popular feature selection methods have been implemented and compared. Experimental results on the several imbalanced data sets, derived from UCI repository database, illustrate the effectiveness of our proposed methods in comparison with the other compared methods in terms of both accuracy and the number of selected features.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
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This document discusses using machine learning clustering algorithms to analyze stock market data. It compares the K-means, COBWEB, DBSCAN, EM and OPTICS clustering algorithms in the WEKA tool on a stock market dataset containing 420 instances and 6 attributes. The K-means algorithm had the best performance with the lowest error and fastest runtime. It clustered the data into 4 groups in 0.16 seconds. The COBWEB algorithm clustered the data into 107 groups in 27.88 seconds. The DBSCAN algorithm found 21 clusters in 3.97 seconds. The paper concludes that K-means is best suited for stock market data mining applications due to its simplicity and speed compared to other algorithms.
Cloudsim a fast clustering-based feature subset selection algorithm for high...ecway
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A fast clustering based feature subset selection algorithm for high-dimension...ecway
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Android a fast clustering-based feature subset selection algorithm for high-...ecway
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Feature Subset Selection for High Dimensional Data using Clustering TechniquesIRJET Journal
The document discusses feature subset selection for high dimensional data using clustering techniques. It proposes a FAST algorithm that has three steps: (1) removing irrelevant features, (2) dividing features into clusters, (3) selecting the most representative feature from each cluster. The FAST algorithm uses DBSCAN, a density-based clustering algorithm, to cluster the features. DBSCAN can identify clusters of arbitrary shape and detect noise, making it suitable for high dimensional data. The goal of feature subset selection is to find a small number of discriminative features that best represent the data.
Optimization Technique for Feature Selection and Classification Using Support...IJTET Journal
Abstract— Classification problems often have a large number of features in the data sets, but only some of them are useful for classification. Data Mining Performance gets reduced by Irrelevant and redundant features. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main objectives are maximizing the classification performance and minimizing the number of features. Moreover, the existing feature selection algorithms treat the task as a single objective problem. Selecting attribute is done by the combination of attribute evaluator and search method using WEKA Machine Learning Tool. We compare SVM classification algorithm to automatically classify the data using selected features with different standard dataset.
IDENTIFICATION AND INVESTIGATION OF THE USER SESSION FOR LAN CONNECTIVITY VIA...ijcseit
This paper mainly presents some technical discussions on the identification and analyze of “LAN usersessions”.
The identification of a user-session is non trivial. Classical methods approaches rely on
threshold based mechanisms. Threshold based techniques are very sensitive to the value chosen for the
threshold, which may be difficult to set correctly. Clustering techniques are used to define a novel
methodology to identify LAN user-sessions without requiring an a priori definition of threshold values. We
have defined a clustering based approach in detail, and also we discussed positive and negative of this
approach, and we apply it to real traffic traces. The proposed methodology is applied to artificially
generated traces to evaluate its benefits against traditional threshold based approaches. We also analyzed
the characteristics of user-sessions extracted by the clustering methodology from real traces and study
their statistical properties.
Correlation of artificial neural network classification and nfrs attribute fi...eSAT Journals
Abstract
Mostly 5 to 15% of the women in the stage of reproduction face the disease called Polycystic Ovarian Syndrome (PCOS) which is the multifaceted, heterogeneous and complex. The long term consequences diseases like endometrial hyperplasia, type 2 diabetes mellitus and coronary disease are caused by the polycystic ovaries, chronic anovulation and hyperandrogenism are characterized with the resistance of insulin and the hypertension, abdominal obesity and dyslipidemia and hyperinsulinemia are called as Metabolic syndrome (frequent metabolic traits) The above cause the common disease called Anovulatory infertility. Computer based information along with advanced Data mining techniques are used for appropriate results. Classification is a classic data mining task, with roots in machine learning. Naïve Bayesian, Artificial Neural Network, Decision Tree, Support Vector Machines are the classification tasks in the data mining. Feature selection methods involve generation of the subset, evaluation of each subset, criteria for stopping the search and validation procedures. The characteristics of the search method used are important with respect to the time efficiency of the feature selection methods. PCA (Principle Component Analysis), Information gain Subset Evaluation, Fuzzy rough set evaluation, Correlation based Feature Selection (CFS) are some of the feature selection techniques, greedy first search, ranker etc are the search algorithms that are used in the feature selection. In this paper, a new algorithm which is based on Fuzzy neural subset evaluation and artificial neural network is proposed which reduces the task of classification and feature selection separately. This algorithm combines the neural fuzzy rough subset evaluation and artificial neural network together for the better performance than doing the tasks separately.
Keywords: ANN, SVM, PCA, CFS
This document discusses online feature selection (OFS) for data mining applications. It addresses two tasks of OFS: 1) learning with full input, where the learner can access all features to select a subset, and 2) learning with partial input, where only a limited number of features can be accessed for each instance. Novel algorithms are presented for each task, and their performance is analyzed theoretically. Experiments on real-world datasets demonstrate the efficacy of the proposed OFS techniques for applications in computer vision, bioinformatics, and other domains involving high-dimensional sequential data.
This document presents a feature clustering algorithm to reduce the dimensionality of feature vectors for text classification. The algorithm groups words in documents into clusters based on similarity, with each cluster characterized by a membership function. Words not similar to existing clusters form new clusters. This avoids specifying features in advance and the need for trial and error. Experimental results showed the method can classify text faster and with better extracted features than other methods.
IRJET- Diverse Approaches for Document Clustering in Product Development Anal...IRJET Journal
This document discusses several approaches for clustering textual documents, including:
1. TF-IDF, word embedding, and K-means clustering are proposed to automatically classify and organize documents.
2. Previous work on document clustering is reviewed, including partition-based techniques like K-means and K-medoids, hierarchical clustering, and approaches using semantic features, PSO optimization, and multi-view clustering.
3. Challenges of clustering large document collections at scale are discussed, along with potential solutions using frameworks like Hadoop.
An integrated mechanism for feature selectionsai kumar
This document discusses an integrated mechanism for feature selection and fuzzy rule extraction for classification problems. It aims to select a useful set of features that can solve the classification problem while designing an interpretable fuzzy rule-based system. The mechanism is an embedded feature selection method, meaning feature selection is integrated into the rule base formation process. This allows it to account for possible nonlinear interactions between features and between features and the modeling tool. The authors demonstrate the effectiveness of the proposed method on several datasets.
Feature selection is a problem closely related to dimensionality reduction. A commonly used
approach in feature selection is ranking the individual features according to some criteria and
then search for an optimal feature subset based on an evaluation criterion to test the optimality.
The objective of this work is to predict more accurately the presence of Learning Disability
(LD) in school-aged children with reduced number of symptoms. For this purpose, a novel
hybrid feature selection approach is proposed by integrating a popular Rough Set based feature
ranking process with a modified backward feature elimination algorithm. The approach follows
a ranking of the symptoms of LD according to their importance in the data domain. Each
symptoms significance or priority values reflect its relative importance to predict LD among the
various cases. Then by eliminating least significant features one by one and evaluating the
feature subset at each stage of the process, an optimal feature subset is generated. The
experimental results shows the success of the proposed method in removing redundant
attributes efficiently from the LD dataset without sacrificing the classification performance.
Similar to Feature Selection Algorithm for Supervised and Semisupervised Clustering (20)
Text Mining in Digital Libraries using OKAPI BM25 ModelEditor IJCATR
The emergence of the internet has made vast amounts of information available and easily accessible online. As a result, most libraries have digitized their content in order to remain relevant to their users and to keep pace with the advancement of the internet. However, these digital libraries have been criticized for using inefficient information retrieval models that do not perform relevance ranking to the retrieved results. This paper proposed the use of OKAPI BM25 model in text mining so as means of improving relevance ranking of digital libraries. Okapi BM25 model was selected because it is a probability-based relevance ranking algorithm. A case study research was conducted and the model design was based on information retrieval processes. The performance of Boolean, vector space, and Okapi BM25 models was compared for data retrieval. Relevant ranked documents were retrieved and displayed at the OPAC framework search page. The results revealed that Okapi BM 25 outperformed Boolean model and Vector Space model. Therefore, this paper proposes the use of Okapi BM25 model to reward terms according to their relative frequencies in a document so as to improve the performance of text mining in digital libraries.
Green Computing, eco trends, climate change, e-waste and eco-friendlyEditor IJCATR
This document discusses green computing practices and sustainable IT services. It provides an overview of factors driving adoption of green computing to reduce costs and environmental impact of data centers, such as rising energy costs and density. Green strategies discussed include improving infrastructure efficiency, power management, thermal management, efficient product design, and virtualization to optimize resource utilization. The document examines how green computing aims to lower costs and environmental footprint, and how sustainable IT services take a broader approach considering economic, environmental and social impacts.
Policies for Green Computing and E-Waste in NigeriaEditor IJCATR
Computers today are an integral part of individuals’ lives all around the world, but unfortunately these devices are toxic to the environment given the materials used, their limited battery life and technological obsolescence. Individuals are concerned about the hazardous materials ever present in computers, even if the importance of various attributes differs, and that a more environment -friendly attitude can be obtained through exposure to educational materials. In this paper, we aim to delineate the problem of e-waste in Nigeria and highlight a series of measures and the advantage they herald for our country and propose a series of action steps to develop in these areas further. It is possible for Nigeria to have an immediate economic stimulus and job creation while moving quickly to abide by the requirements of climate change legislation and energy efficiency directives. The costs of implementing energy efficiency and renewable energy measures are minimal as they are not cash expenditures but rather investments paid back by future, continuous energy savings.
Performance Evaluation of VANETs for Evaluating Node Stability in Dynamic Sce...Editor IJCATR
Vehicular ad hoc networks (VANETs) are a favorable area of exploration which empowers the interconnection amid the movable vehicles and between transportable units (vehicles) and road side units (RSU). In Vehicular Ad Hoc Networks (VANETs), mobile vehicles can be organized into assemblage to promote interconnection links. The assemblage arrangement according to dimensions and geographical extend has serious influence on attribute of interaction .Vehicular ad hoc networks (VANETs) are subclass of mobile Ad-hoc network involving more complex mobility patterns. Because of mobility the topology changes very frequently. This raises a number of technical challenges including the stability of the network .There is a need for assemblage configuration leading to more stable realistic network. The paper provides investigation of various simulation scenarios in which cluster using k-means algorithm are generated and their numbers are varied to find the more stable configuration in real scenario of road.
Optimum Location of DG Units Considering Operation ConditionsEditor IJCATR
The optimal sizing and placement of Distributed Generation units (DG) are becoming very attractive to researchers these days. In this paper a two stage approach has been used for allocation and sizing of DGs in distribution system with time varying load model. The strategic placement of DGs can help in reducing energy losses and improving voltage profile. The proposed work discusses time varying loads that can be useful for selecting the location and optimizing DG operation. The method has the potential to be used for integrating the available DGs by identifying the best locations in a power system. The proposed method has been demonstrated on 9-bus test system.
Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Naïve Bayes Classifi...Editor IJCATR
Early detection of diabetes mellitus (DM) can prevent or inhibit complication. There are several laboratory test that must be done to detect DM. The result of this laboratory test then converted into data training. Data training used in this study generated from UCI Pima Database with 6 attributes that were used to classify positive or negative diabetes. There are various classification methods that are commonly used, and in this study three of them were compared, which were fuzzy KNN, C4.5 algorithm and Naïve Bayes Classifier (NBC) with one identical case. The objective of this study was to create software to classify DM using tested methods and compared the three methods based on accuracy, precision, and recall. The results showed that the best method was Fuzzy KNN with average and maximum accuracy reached 96% and 98%, respectively. In second place, NBC method had respective average and maximum accuracy of 87.5% and 90%. Lastly, C4.5 algorithm had average and maximum accuracy of 79.5% and 86%, respectively.
Web Scraping for Estimating new Record from Source SiteEditor IJCATR
Study in the Competitive field of Intelligent, and studies in the field of Web Scraping, have a symbiotic relationship mutualism. In the information age today, the website serves as a main source. The research focus is on how to get data from websites and how to slow down the intensity of the download. The problem that arises is the website sources are autonomous so that vulnerable changes the structure of the content at any time. The next problem is the system intrusion detection snort installed on the server to detect bot crawler. So the researchers propose the use of the methods of Mining Data Records and the method of Exponential Smoothing so that adaptive to changes in the structure of the content and do a browse or fetch automatically follow the pattern of the occurrences of the news. The results of the tests, with the threshold 0.3 for MDR and similarity threshold score 0.65 for STM, using recall and precision values produce f-measure average 92.6%. While the results of the tests of the exponential estimation smoothing using ? = 0.5 produces MAE 18.2 datarecord duplicate. It slowed down to 3.6 datarecord from 21.8 datarecord results schedule download/fetch fix in an average time of occurrence news.
Evaluating Semantic Similarity between Biomedical Concepts/Classes through S...Editor IJCATR
Most of the existing semantic similarity measures that use ontology structure as their primary source can measure semantic similarity between concepts/classes using single ontology. The ontology-based semantic similarity techniques such as structure-based semantic similarity techniques (Path Length Measure, Wu and Palmer’s Measure, and Leacock and Chodorow’s measure), information content-based similarity techniques (Resnik’s measure, Lin’s measure), and biomedical domain ontology techniques (Al-Mubaid and Nguyen’s measure (SimDist)) were evaluated relative to human experts’ ratings, and compared on sets of concepts using the ICD-10 “V1.0” terminology within the UMLS. The experimental results validate the efficiency of the SemDist technique in single ontology, and demonstrate that SemDist semantic similarity techniques, compared with the existing techniques, gives the best overall results of correlation with experts’ ratings.
Semantic Similarity Measures between Terms in the Biomedical Domain within f...Editor IJCATR
The techniques and tests are tools used to define how measure the goodness of ontology or its resources. The similarity between biomedical classes/concepts is an important task for the biomedical information extraction and knowledge discovery. However, most of the semantic similarity techniques can be adopted to be used in the biomedical domain (UMLS). Many experiments have been conducted to check the applicability of these measures. In this paper, we investigate to measure semantic similarity between two terms within single ontology or multiple ontologies in ICD-10 “V1.0” as primary source, and compare my results to human experts score by correlation coefficient.
A Strategy for Improving the Performance of Small Files in Openstack Swift Editor IJCATR
This is an effective way to improve the storage access performance of small files in Openstack Swift by adding an aggregate storage module. Because Swift will lead to too much disk operation when querying metadata, the transfer performance of plenty of small files is low. In this paper, we propose an aggregated storage strategy (ASS), and implement it in Swift. ASS comprises two parts which include merge storage and index storage. At the first stage, ASS arranges the write request queue in chronological order, and then stores objects in volumes. These volumes are large files that are stored in Swift actually. During the short encounter time, the object-to-volume mapping information is stored in Key-Value store at the second stage. The experimental results show that the ASS can effectively improve Swift's small file transfer performance.
Integrated System for Vehicle Clearance and RegistrationEditor IJCATR
Efficient management and control of government's cash resources rely on government banking arrangements. Nigeria, like many low income countries, employed fragmented systems in handling government receipts and payments. Later in 2016, Nigeria implemented a unified structure as recommended by the IMF, where all government funds are collected in one account would reduce borrowing costs, extend credit and improve government's fiscal policy among other benefits to government. This situation motivated us to embark on this research to design and implement an integrated system for vehicle clearance and registration. This system complies with the new Treasury Single Account policy to enable proper interaction and collaboration among five different level agencies (NCS, FRSC, SBIR, VIO and NPF) saddled with vehicular administration and activities in Nigeria. Since the system is web based, Object Oriented Hypermedia Design Methodology (OOHDM) is used. Tools such as Php, JavaScript, css, html, AJAX and other web development technologies were used. The result is a web based system that gives proper information about a vehicle starting from the exact date of importation to registration and renewal of licensing. Vehicle owner information, custom duty information, plate number registration details, etc. will also be efficiently retrieved from the system by any of the agencies without contacting the other agency at any point in time. Also number plate will no longer be the only means of vehicle identification as it is presently the case in Nigeria, because the unified system will automatically generate and assigned a Unique Vehicle Identification Pin Number (UVIPN) on payment of duty in the system to the vehicle and the UVIPN will be linked to the various agencies in the management information system.
Assessment of the Efficiency of Customer Order Management System: A Case Stu...Editor IJCATR
The Supermarket Management System deals with the automation of buying and selling of good and services. It includes both sales and purchase of items. The project Supermarket Management System is to be developed with the objective of making the system reliable, easier, fast, and more informative.
Energy-Aware Routing in Wireless Sensor Network Using Modified Bi-Directional A*Editor IJCATR
Energy is a key component in the Wireless Sensor Network (WSN)[1]. The system will not be able to run according to its function without the availability of adequate power units. One of the characteristics of wireless sensor network is Limitation energy[2]. A lot of research has been done to develop strategies to overcome this problem. One of them is clustering technique. The popular clustering technique is Low Energy Adaptive Clustering Hierarchy (LEACH)[3]. In LEACH, clustering techniques are used to determine Cluster Head (CH), which will then be assigned to forward packets to Base Station (BS). In this research, we propose other clustering techniques, which utilize the Social Network Analysis approach theory of Betweeness Centrality (BC) which will then be implemented in the Setup phase. While in the Steady-State phase, one of the heuristic searching algorithms, Modified Bi-Directional A* (MBDA *) is implemented. The experiment was performed deploy 100 nodes statically in the 100x100 area, with one Base Station at coordinates (50,50). To find out the reliability of the system, the experiment to do in 5000 rounds. The performance of the designed routing protocol strategy will be tested based on network lifetime, throughput, and residual energy. The results show that BC-MBDA * is better than LEACH. This is influenced by the ways of working LEACH in determining the CH that is dynamic, which is always changing in every data transmission process. This will result in the use of energy, because they always doing any computation to determine CH in every transmission process. In contrast to BC-MBDA *, CH is statically determined, so it can decrease energy usage.
Security in Software Defined Networks (SDN): Challenges and Research Opportun...Editor IJCATR
In networks, the rapidly changing traffic patterns of search engines, Internet of Things (IoT) devices, Big Data and data centers has thrown up new challenges for legacy; existing networks; and prompted the need for a more intelligent and innovative way to dynamically manage traffic and allocate limited network resources. Software Defined Network (SDN) which decouples the control plane from the data plane through network vitalizations aims to address these challenges. This paper has explored the SDN architecture and its implementation with the OpenFlow protocol. It has also assessed some of its benefits over traditional network architectures, security concerns and how it can be addressed in future research and related works in emerging economies such as Nigeria.
Measure the Similarity of Complaint Document Using Cosine Similarity Based on...Editor IJCATR
Report handling on "LAPOR!" (Laporan, Aspirasi dan Pengaduan Online Rakyat) system depending on the system administrator who manually reads every incoming report [3]. Read manually can lead to errors in handling complaints [4] if the data flow is huge and grows rapidly, it needs at least three days to prepare a confirmation and it sensitive to inconsistencies [3]. In this study, the authors propose a model that can measure the identities of the Query (Incoming) with Document (Archive). The authors employed Class-Based Indexing term weighting scheme, and Cosine Similarities to analyse document similarities. CoSimTFIDF, CoSimTFICF and CoSimTFIDFICF values used in classification as feature for K-Nearest Neighbour (K-NN) classifier. The optimum result evaluation is pre-processing employ 75% of training data ratio and 25% of test data with CoSimTFIDF feature. It deliver a high accuracy 84%. The k = 5 value obtain high accuracy 84.12%
Hangul Recognition Using Support Vector MachineEditor IJCATR
The recognition of Hangul Image is more difficult compared with that of Latin. It could be recognized from the structural arrangement. Hangul is arranged from two dimensions while Latin is only from the left to the right. The current research creates a system to convert Hangul image into Latin text in order to use it as a learning material on reading Hangul. In general, image recognition system is divided into three steps. The first step is preprocessing, which includes binarization, segmentation through connected component-labeling method, and thinning with Zhang Suen to decrease some pattern information. The second is receiving the feature from every single image, whose identification process is done through chain code method. The third is recognizing the process using Support Vector Machine (SVM) with some kernels. It works through letter image and Hangul word recognition. It consists of 34 letters, each of which has 15 different patterns. The whole patterns are 510, divided into 3 data scenarios. The highest result achieved is 94,7% using SVM kernel polynomial and radial basis function. The level of recognition result is influenced by many trained data. Whilst the recognition process of Hangul word applies to the type 2 Hangul word with 6 different patterns. The difference of these patterns appears from the change of the font type. The chosen fonts for data training are such as Batang, Dotum, Gaeul, Gulim, Malgun Gothic. Arial Unicode MS is used to test the data. The lowest accuracy is achieved through the use of SVM kernel radial basis function, which is 69%. The same result, 72 %, is given by the SVM kernel linear and polynomial.
Application of 3D Printing in EducationEditor IJCATR
This paper provides a review of literature concerning the application of 3D printing in the education system. The review identifies that 3D Printing is being applied across the Educational levels [1] as well as in Libraries, Laboratories, and Distance education systems. The review also finds that 3D Printing is being used to teach both students and trainers about 3D Printing and to develop 3D Printing skills.
Survey on Energy-Efficient Routing Algorithms for Underwater Wireless Sensor ...Editor IJCATR
In underwater environment, for retrieval of information the routing mechanism is used. In routing mechanism there are three to four types of nodes are used, one is sink node which is deployed on the water surface and can collect the information, courier/super/AUV or dolphin powerful nodes are deployed in the middle of the water for forwarding the packets, ordinary nodes are also forwarder nodes which can be deployed from bottom to surface of the water and source nodes are deployed at the seabed which can extract the valuable information from the bottom of the sea. In underwater environment the battery power of the nodes is limited and that power can be enhanced through better selection of the routing algorithm. This paper focuses the energy-efficient routing algorithms for their routing mechanisms to prolong the battery power of the nodes. This paper also focuses the performance analysis of the energy-efficient algorithms under which we can examine the better performance of the route selection mechanism which can prolong the battery power of the node
Comparative analysis on Void Node Removal Routing algorithms for Underwater W...Editor IJCATR
The designing of routing algorithms faces many challenges in underwater environment like: propagation delay, acoustic channel behaviour, limited bandwidth, high bit error rate, limited battery power, underwater pressure, node mobility, localization 3D deployment, and underwater obstacles (voids). This paper focuses the underwater voids which affects the overall performance of the entire network. The majority of the researchers have used the better approaches for removal of voids through alternate path selection mechanism but still research needs improvement. This paper also focuses the architecture and its operation through merits and demerits of the existing algorithms. This research article further focuses the analytical method of the performance analysis of existing algorithms through which we found the better approach for removal of voids
Decay Property for Solutions to Plate Type Equations with Variable CoefficientsEditor IJCATR
In this paper we consider the initial value problem for a plate type equation with variable coefficients and memory in
1 n R n ), which is of regularity-loss property. By using spectrally resolution, we study the pointwise estimates in the spectral
space of the fundamental solution to the corresponding linear problem. Appealing to this pointwise estimates, we obtain the global
existence and the decay estimates of solutions to the semilinear problem by employing the fixed point theorem
Decay Property for Solutions to Plate Type Equations with Variable Coefficients
Feature Selection Algorithm for Supervised and Semisupervised Clustering
1. International Journal of Computer Applications Technology and Research
Volume 3– Issue 11, 706 - 710, 2014
www.ijcat.com 706
Feature Selection Algorithm for Supervised and
Semisupervised Clustering
S. Gunasekaran
Department of Computer Science and Engineering
V.S.B.Engineering College,
Karur, India
I. Vasudevan
Department of Computer Science and Engineering
V.S.B.Engineering College,
Karur, India
Abstract −In clustering process, semi-supervised learning is a tutorial of contrivance learning methods that make usage of both
labeled and unlabeled data for training - characteristically a trifling quantity of labeled data with a great quantity of unlabeled
data. Semi-supervised learning cascades in the middle of unsupervised learning (without any labeled training data) and
supervised learning (with completely labeled training data). Feature selection encompasses pinpointing a subsection of the most
beneficial features that yields well-suited results as the inventive entire set of features. A feature selection algorithm may be
appraised from both the good organization and usefulness points of view. Although the good organization concerns the time
necessary to discover a subsection of features, the usefulness is related to the excellence of the subsection of features. Traditional
methodologies for clustering data are based on metric resemblances, i.e., non-negative, symmetric, and satisfying the triangle
unfairness measures using graph-based algorithm to replace this process in this project using more recent approaches, like
Affinity Propagation (AP) algorithm can take as input also general non metric similarities.
Keywords: Data mining, Feature selection, Feature clustering, Semi-supervised, Affinity propagation
1. INTRODUCTION
Clustering algorithms can be categorized based
on their cluster model. The most appropriate clustering
algorithm for a particular problem often needs to be chosen
experimentally. It should be designed for one kind of
models has no chance on a dataset that contains a radically
different kind of models. For example, k-means cannot find
non-convex clusters. Difference between classification and
clustering are two common data mining techniques for
finding hidden patterns in data. While the classification and
clustering is often mentioned in the equal sniff, and
dissimilar analytical approaches.
There is diversity of algorithms rummage-sale for
clustering, but all the share belongings of
Iteratively assigning records to a cluster, manipulative a
quantity and re-assigning records to clusters until the
designed procedures don't modification much
demonstrating that the process has converged to firm
sections. Records within a cluster are more comparable to
every one other, and added different from records that are
in other clusters. Contingent on the precise implementation,
there are a diversity of procedures of resemblance that are
rummage-sale to over all aim is for the attitude to converge
to collections of correlated records. Classification is a
dissimilar method than clustering. Classification is
correlated to clustering in that it also segments customer
records into distinctive segments called classes. But
dissimilar clustering, a classification inquiry requires that
the end-user/analyst know ahead of time how classes are
demarcated.
For instance, classes can be demarcated to
represent the probability that a customer nonpayment on a
loan (Yes/No). It is essential that every record in the dataset
rummage-sale to physique the classifier before now have a
value for the trait rummage-sale to describe classes.
Because every record has a value for the trait rummage-sale
to describe the classes, and because the end-user resolves
on the trait to use, classification is much less investigative
than clustering. The impartial of a classifier is not to search
the data to ascertain interesting segments, but relatively to
select how new records should be classified i.e. is this new
customer likely to default on the loan?
With the aim of selecting a subsection of good
features with high opinion to the impartial perceptions,
feature subsection selection is a real way for reducing
dimensionality, rejecting unrelated data, inflammation
learning accurateness, and purifying result unambiguous.
Feature subsection selection can be observed as the
progression of ascertaining and confiscating as various
unrelated and redundant features as possible. This is
because 1) unrelated features do not subsidize to the
extrapolation exactitude and 2) redundant features do not
redound to receiving an enhanced analyst for that they
deliver generally information which is previously
contemporary in other feature(s). Unrelated features, beside
with redundant features, strictly affect the exactness of the
learning technologies.
Thus, feature subsection selection should be able
to identify and remove as much of the unrelated and
redundant information as possible. It develops a novel
algorithm which can efficiently and effectively deal with
both un related and redundant features, and obtain a good
feature subsection. We achieve this through a new feature
selection framework which composed of the two connected
components of unrelated feature removal and redundant
feature removal. The previous acquires features relevant to
2. International Journal of Computer Applications Technology and Research
Volume 3– Issue 11, 706 - 710, 2014
www.ijcat.com 707
the target concept by eliminating unrelated ones, and the
latter removes redundant features from relevant ones via
choosing denotative from different feature clusters, and
thus produces the final subsection.
A fast clustering-based feature selection
algorithm (FAST) works in two steps. In the first step, by
using graph-theoretic clustering methods the features are
separated into clusters. In the second step, the most typical
feature that is powerfully associated to target classes is
designated from every cluster to form a subsection of
features. Features in different clusters are comparatively
independent; the clustering-based approach of FAST has a
high probability of producing a subsection of useful and
sovereign features. To make sure the effectiveness of
FAST, assume the well-organized minimum-spanning tree
(MST) clustering method.
The unrelated feature removal is straightforward
once the right relevance measure is demarcated or selected,
while the redundant feature elimination is a bit of refined.
In the FAST algorithm, it encompasses 1) the structure of
the minimum spanning tree from a weighted complete
graph; 2) the partitioning of the MST into a forest with
every tree denoting a cluster; and 3) the selection of
denotative features from the clusters. Feature selection
encompasses detecting a subsection of the most useful
features that produces compatible results as the original
entire set of features.
2. RELATED WORK
The proposed method [2] provides the number of
features in numerous applications where data has hundreds
or thousands of features. Existing feature selection
approaches predominantly focus on verdict relevant
features. In this feature selection display that feature
relevance alone is inadequate for well-organized feature
selection of high-dimensional data. We define feature
redundancy and propose to perform explicit redundancy
analysis in feature selection. A new framework is
introduced that decouples relevance analysis and
redundancy analysis. We develop a correlation-based
method for relevance and redundancy analysis, and conduct
an empirical study of its efficiency and effectiveness
comparing with representative methods.
The novel algorithm for discovery non-redundant
discarded feature subsections based on the PRBF[5]has
only one consideration, numerical meaning or the
likelihood that the assumption that disseminations of two
features are comparable is true. In the first step directories
have been rummage-sale for ranking, and in the second step
terminated features are detached in an unsupervised way,
because during decrease of terminated features data about
the modules is not used.
The primary tests are promising: on the
reproduction data perfect ranking has been re-formed and
terminated features rejected, while on the real data, with
relatively modest number of features selected outcomes are
regularly the superlative, or close to the superlative,
associating with four state-of-the-art feature selection
algorithms. The novel algorithm appears to work especially
well with the direct SVM classifier. Computational
anxieties of PRBF algorithm are related to other
correlation-based filters, and lower than Relief.
The searching for interacting features in
subsection selection [9] developing and acclimatizing
abilities of robust intellect are superlative established in its
aptitude to learn. Mechanism learning facilitates computer
systems to learn, and recover presentation. Feature
selection facilitates mechanism learning by targeting to
eliminate irrelevant features .Feature interaction presents a
dare to feature subsection selection for cataloging. This is
because a feature by itself might have little relationship
with the objective concept, but when it is combined with
some other features, it can be strongly interrelated with the
objective concept.
Thus, the in advertent elimination of these
features may effect in poor cataloging presentation. It is
computationally inflexible to switch feature exchanges in
general. Nevertheless, the attendance of feature interaction
in an extensive range of real-world requests demands
applied solutions that can decrease high-dimensional data
although perpetuating feature exchanges. In this paper, it
ups the contest to design a special data structure for feature
quality evaluation, and to employ an information-theoretic
feature ranking mechanism to efficiently handle feature
interaction in subset selection.
We conduct experiments to evaluate our
approach by comparing with some representative methods,
perform a lesion study to examine the critical components
of the proposed algorithm to gain insights, and investigate
related issues such as data structure, ranking, time
complexity, and scalability in search of interacting features.
The success of many feature selection algorithms
allows us to tackle challenging real-world problems. Many
applications inherently demand the selection of interacting
features.
An Evaluation on feature selection for text
clustering is first demonstrated that feature selection can
improve the text clustering efficiency and performance in
ideal case, in which features are selected based on class
information. But in real case the class information is
unknown, so only unsupervised feature selection can be
exploited. In many cases, unsupervised feature selection are
much worse than supervised feature selection, not only less
terms they can remove, but also much worse clustering
performance they yield.
3. PROPOSED SYSTEM
Traditional approaches for clustering data are
based on metric resemblances, i.e., nonnegative, symmetric
and filling the triangle disparity measures. More recent
approaches, like Affinity Propagation (AP) algorithm can
take as input also general non metric similarities. AP can
use as input metric selected segments of images’ pairs.
Accordingly, AP has been rummage-sale to solve a wide
3. International Journal of Computer Applications Technology and Research
Volume 3– Issue 11, 706 - 710, 2014
www.ijcat.com 708
range of clustering problems, such as image processing
tasks gene detection tasks, and individual preferences
predictions.
Affinity Propagation is derived as an application
of the max-sum algorithm in issue graph; it is used to
explorations for the smallest amount of dynamism function
on the basis of message passing between data points. In this
system implements the semi supervised learning has taken
a great deal of considerations. It is a mechanism learning
paradigm in which the model is constructed using both
labeled and unlabeled data for training set.
It retrieve the data from training data or labeled
data and extract the feature of the data and compare with
labeled data and unlabeled data .In clustering process,
semi-supervised learning is a class of machine learning
techniques that make use of both labeled and unlabeled
data for training - typically a small amount of labeled data
with a large amount of unlabeled data.
Semi-supervised learning cascades among
unsupervised learning (without any labeled training data)
and supervised learning. Various mechanism-learning
investigators have found that unlabeled data, when
rummage-sale in conjunction with a small amount of
categorized data, can yield substantial development in
learning accuracy.
3.1 Irrelevant Based Feature Selection
A feature selection algorithm may be appraised
from together the proficiency and usefulness point of view.
Although the effectiveness concerns the time requisite to
find a subsection of features, the efficiency is associated to
the excellence of the subsection of features.
Fig 1: Semi-Supervised Learning
Many feature subsection selection algorithms,
some can successfully remove irrelevant features but fail to
handle redundant features yet some of the others can
eliminate the irrelevant while taking care of the redundant
features. In this system the FAST algorithm cascades into
the subsequent group. The previous obtains features
pertinent to the target concept by eliminating unrelated
ones, and then removes redundant features from pertinent
ones via choosing denotative from different feature
clusters.
3.2 Redundant Based Feature Selection
The hybrid methods are combination of filter and
wrapper methods by using a filter method to reduce search
space that will be considered by the succeeding wrapper. It
focuses on coalescing filter and wrapper approaches to
achieve the best possible performance with a particular
learning algorithm with similar time complexity of the filter
methods. Redundant features do not redound to getting a
better predictor for that they provide mostly information
which is already present in other feature(s).
3.3 Graph Based Cluster
An algorithm to systematically add instance-level
constraints to the graph based clustering algorithm. Unlike
other algorithms which use a given static modeling
parameters to find clusters, Graph based cluster algorithm
finds clusters by dynamic modeling. Graph based cluster
algorithm uses both Closeness and interconnectivity while
identifying the most similar pair of clusters to be merged.
3.4 Affinity Propagation Algorithm
The affinity propagation (AP) is a clustering
algorithm established on the notion of "message passing"
among data points. For example of clustering algorithm is
k-means. It does not need the quantity of clusters to be
determined or estimated before running the algorithm.
Let x₁ and x be a set of data points, with no
expectations ready around their internal structure, and the
function that measures the resemblance among any two
points, that is s(xᵢ, x) >s(xᵢ, x) if x is further related to xᵢ than
x.
Fig 2: Process of clustering
Process of text clustering
Removal of irrelevant word from
documnets
Text document
4. International Journal of Computer Applications Technology and Research
Volume 3– Issue 11, 706 - 710, 2014
www.ijcat.com 709
Fig 3: system flow diagram for proposed system
The algorithm ensues by flashing two message passing
steps, it modernize by using the subsequent two conditions:
The "responsibility" conditions R has values r(j, n)
that measure how well-matched x is to aid as the
exemplar for x, comparative to other candidate
exemplars for x.
The "availability" conditions A contains values a(j, n)
characterizes how "applicable" it would be for x to
pick x as its exemplar, taking into interpretation other
points' favorite for x as an exemplar.
Together conditions are reset to all zeroes, and can be
regarded as probability counters. The following updates are
iteratively used to perform the algorithm:
First, responsibility updates are sent around:
r(j,n) s(j,n) - { ’ ’ }
Then, availability is updated per
a(j,n) (
∑
)
a(n,n) ∑ ( )
4. EXPERIMENTAL RESULTS
The performance of the proposed algorithm is
compared with the two well-known feature selection
algorithms FCBF and CFS of text data from the aspects of
the proportion of selected features and runtime analysis.
TABLE 1 Runtime (in ms) of the Feature Selection
Algorithms
The affinity propagation algorithm is used to
reduce the runtime compare with the graph based algorithm
of FAST. It reduces the error and simplicity of
performance. The semi-supervised learning is a tutorial of
contrivance learning methods that make usage of both
labeled and unlabeled data for training - characteristically a
trifling quantity of labeled data with a great quantity of
unlabeled data.
It is used to improve the efficiency of feature
selection of FAST algorithm. Affinity propagation
algorithm is used to achieve good performance of
processing time. It provides better results with less amount
of time compare with graph based algorithm.
Fig 3: Runtime (in ms) of the Feature Selection Algorithms
0
20
40
60
80
100
120
Chess
Elephant
Wap.wc
Colon
GCM
AR10P
B-cell1
FAST(Affinity
Propagation)
FAST(Graph
Based)
FCBF
CFS
Data
set
FAST
(Affinity
Propagation)
FAST
(Graph
Based) FCBF CFS
Chess 90.1 94.02 94.02 90.43
Elephant 95.35 98.09 99.94 99.97
Wap.wc 69.01 71.25 75.74 77.8
Colon 87.4 90.45 90.76 89.14
GCM 55.69 58.73 59.16 60.92
AR10P 74.05 77.69 75.54 79.54
B-cell1 79.21 81.01 82.94 87.33
5. International Journal of Computer Applications Technology and Research
Volume 3– Issue 11, 706 - 710, 2014
www.ijcat.com 710
5. CONCLUSION
In this paper, the semi supervised learning
retrieve the data from training data or labeled data and
extracts the feature of the data and compare with labeled
data and unlabeled data. Feature selection encompasses
pinpointing a subsection of the most beneficial features that
yields well-suited results as the inventive entire set of
features. A feature selection algorithm may be appraised
from both the good organization and usefulness points of
view. Then we use Affinity propagation algorithm for low
error, high speed, flexible, and remarkably simple
clustering algorithm that may be rummage-sale in forming
teams of participants for business simulations and
experiential exercises, and in organizing participants’
preferences for the parameters of simulations.
5. REFERENCES
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Clustering-Based Feature Subset Selection Algorithm for
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