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Benefiting feature selection by the discovery of false irrelevant attributes
Chao L.S.1; Wong D.F.1; Chen P.C.L.1; Ng W.W.Y.2; Yeung D.S.2
2015
Source PublicationInternational Journal of Wavelets, Multiresolution and Information Processing
ISSN02196913
Volume13Issue:4
Abstract

The ordinary feature selection methods select only the explicit relevant attributes by filtering the irrelevant ones. They trade the selection accuracy for the execution time and complexity. In which, the hidden supportive information possessed by the irrelevant attributes may be lost, so that they may miss some good combinations. We believe that attributes are useless regarding the classification task by themselves, sometimes may provide potentially useful supportive information to other attributes and thus benefit the classification task. Such a strategy can minimize the information lost, therefore is able to maximize the classification accuracy. Especially for the dataset contains hidden interactions among attributes. This paper proposes a feature selection methodology from a new angle that selects not only the relevant features, but also targeting at the potentially useful false irrelevant attributes by measuring their supportive importance to other attributes. The empirical results validate the hypothesis by demonstrating that the proposed approach outperforms most of the state-of-the-art filter based feature selection methods.

KeywordData Mining Data Preprocessing Feature Selection Hidden Interaction Supportive Relevance
DOI10.1142/S021969131550023X
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Mathematics
WOS SubjectComputer Science, Software Engineering ; Mathematics, Interdisciplinary Applications
WOS IDWOS:000358621600005
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Universidade de Macau
2.South China University of Technology
Recommended Citation
GB/T 7714
Chao L.S.,Wong D.F.,Chen P.C.L.,et al. Benefiting feature selection by the discovery of false irrelevant attributes[J]. International Journal of Wavelets, Multiresolution and Information Processing,2015,13(4).
APA Chao L.S.,Wong D.F.,Chen P.C.L.,Ng W.W.Y.,&Yeung D.S..(2015).Benefiting feature selection by the discovery of false irrelevant attributes.International Journal of Wavelets, Multiresolution and Information Processing,13(4).
MLA Chao L.S.,et al."Benefiting feature selection by the discovery of false irrelevant attributes".International Journal of Wavelets, Multiresolution and Information Processing 13.4(2015).
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