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.
A Review on Feature Selection Methods For Classification TasksEditor IJCATR
In recent years, application of feature selection methods in medical datasets has greatly increased. The challenging task in
feature selection is how to obtain an optimal subset of relevant and non redundant features which will give an optimal solution without
increasing the complexity of the modeling task. Thus, there is a need to make practitioners aware of feature selection methods that have
been successfully applied in medical data sets and highlight future trends in this area. The findings indicate that most existing feature
selection methods depend on univariate ranking that does not take into account interactions between variables, overlook stability of the
selection algorithms and the methods that produce good accuracy employ more number of features. However, developing a universal
method that achieves the best classification accuracy with fewer features is still an open research area.
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
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
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.
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
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Minkowski Distance based Feature Selection Algorithm for Effective Intrusion ...IJMER
Intrusion Detection System (IDS) plays a major role in the provision of effective security to various types of networks. Moreover, Intrusion Detection System for networks need appropriate rule set for classifying network bench mark data into normal or attack patterns. Generally, each dataset is characterized by a large set of features. However, all these features will not be relevant or fully contribute in identifying an attack. Since different attacks need various subsets to provide better detection accuracy. In this paper an improved feature selection algorithm is proposed to identify the most appropriate subset of features for detecting a certain attacks. This proposed method is based on Minkowski distance feature ranking and an improved exhaustive search that selects a better combination of features. This system has been evaluated using the KDD CUP 1999 dataset and also with EMSVM [1] classifier. The experimental results show that the proposed system provides high classification accuracy and low false alarm rate when applied on the reduced feature subsets
Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...CSCJournals
This paper presents the application of multi dimensional feature reduction of Consistency Subset Evaluator (CSE) and Principal Component Analysis (PCA) and Unsupervised Expectation Maximization (UEM) classifier for imaging surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the UEM classifier is performed to focus on the need of detecting dangerous weapons. However, CSE and PCA are used to explore the usefulness of each feature and reduce the multi dimensional features to simplified features with no underlying hidden structure. In this paper, we take advantage of the simplified features and classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of the UEM classifier, several classifiers are used to compare the overall accuracy of the system with the compliment from the features reduction of CSE and PCA. These unsupervised classifiers include Farthest First, Densitybased Clustering and k-Means methods. The final outcome of this research clearly indicates that UEM has the ability in improving the classification accuracy using the extracted features from the multi-dimensional feature reduction of CSE. Besides, it is also shown that PCA is able to speed-up the computational time with the reduced dimensionality of the features compromising the slight decrease of accuracy.
A Review on Feature Selection Methods For Classification TasksEditor IJCATR
In recent years, application of feature selection methods in medical datasets has greatly increased. The challenging task in
feature selection is how to obtain an optimal subset of relevant and non redundant features which will give an optimal solution without
increasing the complexity of the modeling task. Thus, there is a need to make practitioners aware of feature selection methods that have
been successfully applied in medical data sets and highlight future trends in this area. The findings indicate that most existing feature
selection methods depend on univariate ranking that does not take into account interactions between variables, overlook stability of the
selection algorithms and the methods that produce good accuracy employ more number of features. However, developing a universal
method that achieves the best classification accuracy with fewer features is still an open research area.
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
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
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.
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
Minkowski Distance based Feature Selection Algorithm for Effective Intrusion ...IJMER
Intrusion Detection System (IDS) plays a major role in the provision of effective security to various types of networks. Moreover, Intrusion Detection System for networks need appropriate rule set for classifying network bench mark data into normal or attack patterns. Generally, each dataset is characterized by a large set of features. However, all these features will not be relevant or fully contribute in identifying an attack. Since different attacks need various subsets to provide better detection accuracy. In this paper an improved feature selection algorithm is proposed to identify the most appropriate subset of features for detecting a certain attacks. This proposed method is based on Minkowski distance feature ranking and an improved exhaustive search that selects a better combination of features. This system has been evaluated using the KDD CUP 1999 dataset and also with EMSVM [1] classifier. The experimental results show that the proposed system provides high classification accuracy and low false alarm rate when applied on the reduced feature subsets
Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...CSCJournals
This paper presents the application of multi dimensional feature reduction of Consistency Subset Evaluator (CSE) and Principal Component Analysis (PCA) and Unsupervised Expectation Maximization (UEM) classifier for imaging surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the UEM classifier is performed to focus on the need of detecting dangerous weapons. However, CSE and PCA are used to explore the usefulness of each feature and reduce the multi dimensional features to simplified features with no underlying hidden structure. In this paper, we take advantage of the simplified features and classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of the UEM classifier, several classifiers are used to compare the overall accuracy of the system with the compliment from the features reduction of CSE and PCA. These unsupervised classifiers include Farthest First, Densitybased Clustering and k-Means methods. The final outcome of this research clearly indicates that UEM has the ability in improving the classification accuracy using the extracted features from the multi-dimensional feature reduction of CSE. Besides, it is also shown that PCA is able to speed-up the computational time with the reduced dimensionality of the features compromising the slight decrease of accuracy.
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.
Feature selection is one of the most fundamental steps in machine learning. It is 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 process of feature ranking
follows a method of calculating the significance or priority of each symptoms of LD as per their
contribution in representing the knowledge contained in the dataset. 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. For comparative analysis and to establish the importance of rough set
theory in feature selection, the backward feature elimination algorithm is combined with two state-of-theart
filter based feature ranking techniques viz. information gain and gain ratio. The experimental results
show the proposed feature selection approach outperforms the other two in terms of the data reduction.
Also, the proposed method eliminates all the redundant attributes efficiently from the LD dataset without
sacrificing the classification performance.
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
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
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The comparison of the text classification methods to be used for the analysis...ijcsit
Text classification is used for the purpose of preventing the leakage of the data which is highly important
within the institution through unallowed ways. The results obtained from the text classification process
should be integrated into the DLP architecture immediately. The data flowing through the net requires
instant control and the flow of the sensitive data should be prevented. The use of the machinery learning
methods is required to perform the text classification which will be integrated into the DLP architecture.
The experimental results of the comparison of text classification methods to be used in the interface written
on the ICAP protocol have been prepared in the networked architecture developed for the DLP system.
Also, the choice of the text classification method to be used in the instant control of the sensitive data has
been carried out. The DLP text classification architecture developed helps decide the classification method
through the examination of the data in motion. The method to be chosen for the text classification is applied
to the ICAP protocol, and the analysis of the sensitive data and confidentiality are provided.
Optimal feature selection from v mware esxi 5.1 feature setijccmsjournal
A study of VMware ESXi 5.1 server has been carried out to find the optimal set of parameters which
suggest usage of different resources of the server. Feature selection algorithms have been used to extract
the optimum set of parameters of the data obtained from VMware ESXi 5.1 server using esxtop command.
Multiple virtual machines (VMs) are running in the mentioned server. K-means algorithm is used for
clustering the VMs. The goodness of each cluster is determined by Davies Bouldin index and Dunn index
respectively. The best cluster is further identified by the determined indices. The features of the best cluster
are considered into a set of optimal parameters.
An introduction to variable and feature selectionMarco Meoni
Presentation of a great paper from Isabelle Guyon (Clopinet) and André Elisseeff (Max Planck Institute) back in 2003, which outlines the main techniques for feature selection and model validation in machine learning 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
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 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.
New Feature Selection Model Based Ensemble Rule Classifiers Method for Datase...ijaia
Feature selection and classification task are an essential process in dealing with large data sets that
comprise numerous number of input attributes. There are many search methods and classifiers that have
been used to find the optimal number of attributes. The aim of this paper is to find the optimal set of
attributes and improve the classification accuracy by adopting ensemble rule classifiers method. Research
process involves 2 phases; finding the optimal set of attributes and ensemble classifiers method for
classification task. Results are in terms of percentage of accuracy and number of selected attributes and
rules generated. 6 datasets were used for the experiment. The final output is an optimal set of attributes
with ensemble rule classifiers method. The experimental results conducted on public real dataset
demonstrate that the ensemble rule classifiers methods consistently show improve classification accuracy
on the selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
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.
Feature selection is one of the most fundamental steps in machine learning. It is 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 process of feature ranking
follows a method of calculating the significance or priority of each symptoms of LD as per their
contribution in representing the knowledge contained in the dataset. 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. For comparative analysis and to establish the importance of rough set
theory in feature selection, the backward feature elimination algorithm is combined with two state-of-theart
filter based feature ranking techniques viz. information gain and gain ratio. The experimental results
show the proposed feature selection approach outperforms the other two in terms of the data reduction.
Also, the proposed method eliminates all the redundant attributes efficiently from the LD dataset without
sacrificing the classification performance.
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
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
The comparison of the text classification methods to be used for the analysis...ijcsit
Text classification is used for the purpose of preventing the leakage of the data which is highly important
within the institution through unallowed ways. The results obtained from the text classification process
should be integrated into the DLP architecture immediately. The data flowing through the net requires
instant control and the flow of the sensitive data should be prevented. The use of the machinery learning
methods is required to perform the text classification which will be integrated into the DLP architecture.
The experimental results of the comparison of text classification methods to be used in the interface written
on the ICAP protocol have been prepared in the networked architecture developed for the DLP system.
Also, the choice of the text classification method to be used in the instant control of the sensitive data has
been carried out. The DLP text classification architecture developed helps decide the classification method
through the examination of the data in motion. The method to be chosen for the text classification is applied
to the ICAP protocol, and the analysis of the sensitive data and confidentiality are provided.
Optimal feature selection from v mware esxi 5.1 feature setijccmsjournal
A study of VMware ESXi 5.1 server has been carried out to find the optimal set of parameters which
suggest usage of different resources of the server. Feature selection algorithms have been used to extract
the optimum set of parameters of the data obtained from VMware ESXi 5.1 server using esxtop command.
Multiple virtual machines (VMs) are running in the mentioned server. K-means algorithm is used for
clustering the VMs. The goodness of each cluster is determined by Davies Bouldin index and Dunn index
respectively. The best cluster is further identified by the determined indices. The features of the best cluster
are considered into a set of optimal parameters.
An introduction to variable and feature selectionMarco Meoni
Presentation of a great paper from Isabelle Guyon (Clopinet) and André Elisseeff (Max Planck Institute) back in 2003, which outlines the main techniques for feature selection and model validation in machine learning 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
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 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.
New Feature Selection Model Based Ensemble Rule Classifiers Method for Datase...ijaia
Feature selection and classification task are an essential process in dealing with large data sets that
comprise numerous number of input attributes. There are many search methods and classifiers that have
been used to find the optimal number of attributes. The aim of this paper is to find the optimal set of
attributes and improve the classification accuracy by adopting ensemble rule classifiers method. Research
process involves 2 phases; finding the optimal set of attributes and ensemble classifiers method for
classification task. Results are in terms of percentage of accuracy and number of selected attributes and
rules generated. 6 datasets were used for the experiment. The final output is an optimal set of attributes
with ensemble rule classifiers method. The experimental results conducted on public real dataset
demonstrate that the ensemble rule classifiers methods consistently show improve classification accuracy
on the selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
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
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.
ATTRIBUTE REDUCTION-BASED ENSEMBLE RULE CLASSIFIERS METHOD FOR DATASET CLASSI...csandit
Attribute reduction and classification task are an essential process in dealing with large data
sets that comprise numerous number of input attributes. There are many search methods and
classifiers that have been used to find the optimal number of attributes. The aim of this paper is
to find the optimal set of attributes and improve the classification accuracy by adopting
ensemble rule classifiers method. Research process involves 2 phases; finding the optimal set of
attributes and ensemble classifiers method for classification task. Results are in terms of
percentage of accuracy and number of selected attributes and rules generated. 6 datasets were
used for the experiment. The final output is an optimal set of attributes with ensemble rule
classifiers method. The experimental results conducted on public real dataset demonstrate that
the ensemble rule classifiers methods consistently show improve classification accuracy on the
selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
An unsupervised feature selection algorithm with feature ranking for maximizi...Asir Singh
Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,
efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical,
finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The
predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and
redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be
removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm
namely unsupervised learning with ranking based feature selection (FSULR). It removes redundant features by clustering and eliminates
irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this
proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes (NB),
instance based (IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for
classifiers.
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.
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.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
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.