Estimating Optimal Feature Subsets Using Mutual Information Feature Selector and Rough Sets

Authors:
Sombut Foitong;Pornthep Rojanavasu;Boonwat Attachoo;Ouen Pinngern
Affiliations:
Department of Computer Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand 10200;Department of Computer Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand 10200;Department of Computer Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand 10200;Department of Computer Science, Faculty of Science, Ramkhamhaeng University, Bangkok, Thailand 10240
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

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Abstract

Mutual Information (MI) is a good selector of relevance between input and output feature and have been used as a measure for ranking features in several feature selection methods. Theses methods cannot estimate optimal feature subsets by themselves, but depend on user defined performance. In this paper, we propose estimation of optimal feature subsets by using rough sets to determine candidate feature subset which receives from MI feature selector. The experiment shows that we can correct nonlinear problems and problems in situation of two or more combined features are dominant features, maintain an improve classification accuracy.