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Bat algorithm for rough set attribute reduction

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dc.citedby36
dc.contributor.authorTaha A.M.en_US
dc.contributor.authorTang A.Y.C.en_US
dc.contributor.authorid55699699200en_US
dc.contributor.authorid36806985400en_US
dc.date.accessioned2023-12-29T07:45:27Z
dc.date.available2023-12-29T07:45:27Z
dc.date.issued2013
dc.description.abstractAttribute reduction (AR) refers to the problem of choosing an optimal subset of attributes from a larger set of possible attributes that are most predictive for a given result. AR techniques have recently attracted attention due to its importance in many areas such as pattern recognition, machine learning and signal processing. In this paper, a new optimization method has been introduced called bat algorithm for attribute reduction (BAAR), the proposed method is based mainly on the echolocation behavior of bats. BAAR is verified using 13 benchmark datasets. Experimental results show that the performances of the proposed method when compared to other features selection methods achieve equal or better performance. � 2005 - 2013 JATIT & LLS. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.epage8
dc.identifier.issue1
dc.identifier.scopus2-s2.0-84877748909
dc.identifier.spage1
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84877748909&partnerID=40&md5=c7a7697d1cd210ff8db1aef91ab51fb1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30200
dc.identifier.volume51
dc.pagecount7
dc.publisherAsian Research Publishing Network (ARPN)en_US
dc.sourceScopus
dc.sourcetitleJournal of Theoretical and Applied Information Technology
dc.subjectAttribute reduction
dc.subjectBat algorithm
dc.subjectNature inspired
dc.subjectRough set
dc.titleBat algorithm for rough set attribute reductionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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