Estimating optimal feature subsets using mutual information feature selector and rough sets

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dc.contributor.authorFoitong, Sombut
dc.contributor.authorRojanavasu, Pornthep
dc.contributor.authorAttachoo, Boonwat
dc.contributor.authorPinngern, Ouen
dc.date.accessioned2026-08-06T09:58:08Z
dc.date.available2026-08-06T09:58:08Z
dc.date.issued2009-01-01
dc.description.abstractMutual 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.
dc.identifier.citationLecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 5476 LNAI, 973-980, 2009
dc.identifier.doi10.1007/978-3-642-01307-2_103
dc.identifier.issn03029743
dc.identifier.other2-s2.0-67650705016
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/2451
dc.sourceLecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
dc.subjectC4.5
dc.subjectMLP
dc.subjectMutual Information
dc.subjectNonlinear Problems
dc.subjectRough sets
dc.titleEstimating optimal feature subsets using mutual information feature selector and rough sets
dc.typeConference Paper

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