Call for Paper - March 2023 Edition
IJCA solicits original research papers for the March 2023 Edition. Last date of manuscript submission is February 20, 2023. Read More

An Efficient Feature Selection Technique using Supervised Fuzzy Information Theory

Print
PDF
International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 85 - Number 19
Year of Publication: 2014
Authors:
B. Azhagu Sundari
Antony Selvadoss Thanamani
10.5120/15099-3283

Azhagu B Sundari and Antony Selvadoss Thanamani. Article: An Efficient Feature Selection Technique using Supervised Fuzzy Information Theory. International Journal of Computer Applications 85(19):40-45, January 2014. Full text available. BibTeX

@article{key:article,
	author = {B. Azhagu Sundari and Antony Selvadoss Thanamani},
	title = {Article: An Efficient Feature Selection Technique using Supervised Fuzzy Information Theory},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {85},
	number = {19},
	pages = {40-45},
	month = {January},
	note = {Full text available}
}

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 ] 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. Farid, Jerome Darmont, Nouria Harbi, Nguyen Huu Hoa and Mohammad Zahidur Rahman ," Adaptive Network Intrusion Detection Learning: Attribute Selection and Classification" , International Conference on computer systems Engineering(ICCSE 2009). [ 4 ] Wa'el M. Mahmud, Hamdy N. Agiza, and Elsayed Radwan (October 2009) ,Intrusion Detection Using Rough Sets based Parallel Genetic Algorithm Hybrid Model, Proceedings of the World Congress on Engineering and Computer Science 2009 Vol II WCECS 2009, San Francisco, USA [ 5 ] Thangavel, K. , & Pethalakshmi, A. Elseviewer (2009). , Dimensionality reduction based on rough set theory 9, 1-12. doi: 10. 1016/j. asoc. 2008. 05. 006. [ 6 ] Kun-Ming Yu, Ming-Feng Wu,and Wai-Tak Wong (April,2008), Protocol-Based Classification for Intrusion Detection, APPLIED COMPUTER & APPLIED COMPUTATIONAL SCIENCE (ACACOS '08), Hangzhou, China. [ 7 ] Shaik Akbar, Dr. K. Nageswara Rao ,Dr. J. A. Chandulal (August 2010),Intrusion Detection System Methodologies Based on Data Analysis, International Journal of Computer Applications (0975 – 8887) Volume 5– No. 2. [ 8 ] Chuzhou University,China, Guangshun Yao, Chuanjian Yang,1Lisheng Ma, Qian Ren (June 2011) An New Algorithm of Modifying Hu's Discernibility Matrix and its Attribute Reduction, International Journal of Advancements in Computing Technology Volume 3, Number 5 [ 9 ] T. Subbulakshmi , A. Ramamoorthi, and Dr. S. Mercy Shalinie(August 2009), Ensemble design for intrusion detection systems, International Journal of Computer science & Information Technology (IJCSIT), Vol 1, No 1. [ 10 ] Y. Y. Yao and Y. Zhao (2009), Discernibility matrix simplication for constructing attribute reducts, Information Sciences, Vol. 179, No. 5, 867-882. [ 11 ] Jen-Da Shie • Shyi-Ming Chen (Feb 2007),Feature subset selection based on fuzzy entropy measures for handling classi?cation problems, Appl Intell (2008) 28: 69–82,DOI 10. 1007/s10489-007-0042-6. [ 12 ] Kosko B (1986) Fuzzy entropy and conditioning. Inf Scie 40(2):165–174 [ 13 ] Lee HM, Chen CM, Chen JM, Jou YL (2001) An efficient fuzzy classifier with feature selection based on fuzzy entropy. IEEE Trans Syst Man Cybern Part B Cybern 31(3):426–432 [ 14 ] Luca AD, Termini S (1972) A definition of a non-probabilistic entropy in the setting of fuzzy set theory. Inf Control 20(4):301– 312 [ 15 ] Shannon CE (1948) A mathematical theory of communication. Bell Syst Techn J 27(3):379–423 [ 16 ] Hahn-Ming Lee, Member, IEEE, Chih-Ming Chen Jyh-Ming Chen, and Yu-Lu Jou , An Efficient Fuzzy Classifier with Feature Selection Based on Fuzzy Entropy . [ 17 ] Hamid Parvin, Behrouz Minaei Bidgoli,hossein ghaffarin , An Innovative Feature Selection Using Fuzzy Entropy,Advances in Neural Networks – ISNN 2011 [ 18 ] S. Sethuramalingam, Dr. E. R. Naganathan, Hybrid Feature selection for Network Intrusion Detection",International Journal Of Computer Science and Engineering. Volume 3, issue 5, PP-1773-1780,2011. [ 19 ] B. Azhagusundari, Dr. Antony Selvadoss Thanamani, "Feature Selection based on Information Gain", International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075,Volume-2, Issue-2, pp:18-21, January 2013. [ 20 ] B. Azhagusundari, Dr. Antony Selvadoss Thanamani, "Feature selection based on Fuzzy Entropy", International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), ISSN 2278-6856, Volume 2, Issue 2, pp:30-34, March – April 2013.