The proposed algorithm for feature selection based on rough set and mutual information

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dc.contributor.authorOunsrimuang, Pimolrat
dc.contributor.authorBoonjing, Veera
dc.date.accessioned2026-08-06T10:08:51Z
dc.date.available2026-08-06T10:08:51Z
dc.date.issued2014-01-01
dc.description.abstractThe feature selection approaches based on rough set theory aim to reduce the input data for improvement classification accuracy. Most existing approaches have concerned the discernibility relation to find the features, and have employed the dependency function for measuring the goodness of feature. The most relevant information cannot be visible by using information from discernibility relation only, so that neglecting indiscernibility relation, mostly relevant may be invisible. Moreover, their results are not tolerant to noisy or inconsistency data. Therefore, this paper proposes new algorithm based on rough set theory, which concerned both the discernibility and indiscernibility relations. The experimental results show that our approach gives higher classification accuracy than existing approaches. © 2014 Pushpa Publishing House, Allahabad, India.
dc.identifier.citationFar East Journal of Mathematical Sciences, 88(2), 199-216, 2014
dc.identifier.issn09720871
dc.identifier.other2-s2.0-84902786482
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/5490
dc.sourceFar East Journal of Mathematical Sciences
dc.subjectClassification
dc.subjectDiscernibility relation
dc.subjectFeature selection
dc.subjectIndiscernibility relation
dc.subjectMutual information
dc.subjectRough sets
dc.titleThe proposed algorithm for feature selection based on rough set and mutual information
dc.typeArticle

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