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Reseach Article

An Efficient Feature Selection Technique using Supervised Fuzzy Information Theory

by B. Azhagu Sundari, Antony Selvadoss Thanamani
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 85 - Number 19
Year of Publication: 2014
Authors: B. Azhagu Sundari, Antony Selvadoss Thanamani
10.5120/15099-3283

B. Azhagu Sundari, Antony Selvadoss Thanamani . An Efficient Feature Selection Technique using Supervised Fuzzy Information Theory. International Journal of Computer Applications. 85, 19 ( January 2014), 40-45. DOI=10.5120/15099-3283

@article{ 10.5120/15099-3283,
author = { B. Azhagu Sundari, Antony Selvadoss Thanamani },
title = { An Efficient Feature Selection Technique using Supervised Fuzzy Information Theory },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 85 },
number = { 19 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 40-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume85/number19/15099-3283/ },
doi = { 10.5120/15099-3283 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:02:56.279385+05:30
%A B. Azhagu Sundari
%A Antony Selvadoss Thanamani
%T An Efficient Feature Selection Technique using Supervised Fuzzy Information Theory
%J International Journal of Computer Applications
%@ 0975-8887
%V 85
%N 19
%P 40-45
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Feature Selection is one of the key processes for knowledge acquisition. Some data set is multidimensional and larger in size. When this data set is used for classification it may end with wrong results and it may also occupy more resources especially in terms of time. Most of the features present are redundant and inconsistent and affect the classification. In order to improve the efficiency of classification these redundancy and inconsistency features must be eliminated. The Feature subset contains the minimum number of features that most contribute to accuracy In this paper, present a method for dealing with feature subset selection based on fuzzy Information measures for handling classification problems. First, to construct the membership function of each fuzzy set of a feature. Then, select the feature subset based on the proposed fuzzy Informationy measure focusing on boundary samples. It also presents an experiment result to show the applicability of the proposed method. The performance of the system is evaluated in MATLAB on several benchmark data sets in the UCI machine learning repository.

References
  1. <ul style="text-align: justify;"> [ 1 ] Meysam. Madani, Zalireza. Nowroozi,"Using Information Theory in Pattern Recognition for Intrusion Detection" , Journal of Theoretical and Applied Information Technology ISSN :1992-8645 December 2011. Vol. 34 no. 2 [ 2 ] Yogendra Kumar Jain , Upendra, "An Efficient Intrusion Detection Based on Decision Tree Classifier Using Feature Reduction",International Journal of Scientific and Research Publications, Volume 2,Issue 1 , January 2012. [ 3 ] Dewan Md.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Entropy Data Mining Attribute Reduction Feature selection Information Theory