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Estimating optimal feature subsets using mutual information feature selector and rough sets
Author(s)
Foitong, Sombut
Rojanavasu, Pornthep
Attachoo, Boonwat
Pinngern, Ouen
Date Issued
January 1, 2009
Type
Conference Paper
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. © Springer-Verlag Berlin Heidelberg 2009.
Citation
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 5476 LNAI, 973-980, 2009