ATTRIBUTE REDUCTION BY PARTITIONING THE MINIMIZED DISCERNIBILITY FUNCTION

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dc.contributor.authorKahramanli, Sirzat
dc.contributor.authorHacibeyoglu, Mehmet
dc.contributor.authorArslan, Ahmet
dc.date.accessioned2020-03-26T18:13:50Z
dc.date.available2020-03-26T18:13:50Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe goal of attribute reduction is to reduce the problem size and search space for learning algorithms. The basic solution of this problem is to generate all possible minimal attributes subsets (MASes) and choose one of them, with minimal size. This can be done by constructing a kind of discernibility function (DF) from the dataset and converting it to disjunctive normal form (DNF). Since this conversion is NP-hard, for attribute reduction usually heuristic algorithms are used. But these algorithms generate one or a small number of possible MASes that generally is not sufficient for optimality of dataset processing in such aspects as the simplicity of data representation and description, the speed and classification accuracy of the data mining algorithms and the required amount of memory. In this study, we propose an algorithm that finds all MASes by iteratively partitioning the DF so that the part to be converted to DNF in each of iterations has the space complexity no higher than the square root of the worst-case space complexity of the conversion of the whole DF to DNF. The number of iterations is always fewer than the number of attributes.en_US
dc.description.sponsorshipSelcuk University Konya, TurkeySelcuk Universityen_US
dc.description.sponsorshipThis study is supported by Selcuk University Scientific Research Projects Coordinatorship/Konya, Turkey. The authors would like to thank the editors and anonymous reviewers of this manuscript for their very helpful suggestions.en_US
dc.identifier.endpage2186en_US
dc.identifier.issn1349-4198en_US
dc.identifier.issn1349-418Xen_US
dc.identifier.issue5Aen_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage2167en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/26180
dc.identifier.volume7en_US
dc.identifier.wosWOS:000290601100012en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherICIC INTERNATIONALen_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROLen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectAttribute reductionen_US
dc.subjectFeature selectionen_US
dc.subjectDiscernibility functionen_US
dc.subjectFunctional partitioningen_US
dc.titleATTRIBUTE REDUCTION BY PARTITIONING THE MINIMIZED DISCERNIBILITY FUNCTIONen_US
dc.typeArticleen_US

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