Feature selection through binary brain storm optimization

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dc.contributor.authorPapa, João P. [UNESP]
dc.contributor.authorRosa, Gustavo H. [UNESP]
dc.contributor.authorde Souza, André N. [UNESP]
dc.contributor.authorAfonso, Luis C.S.
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.date.accessioned2019-10-06T16:53:37Z
dc.date.available2019-10-06T16:53:37Z
dc.date.issued2018-11-01
dc.description.abstractFeature selection stands for the process of finding the most relevant subset of features based on some criterion, which turns out to be an optimization task. In this context, several metaheuristic techniques have been extensively studied achieving results comparable to some state-of-the-art and traditional optimization techniques. This paper introduces a variation of the Brain Storm Optimization (i.e., Binary Brain Storm Optimization) for feature selection purposes, where real-valued solutions are mapped onto a boolean hypercube using different transfer functions. The proposed Binary Brain Storm Optimization was evaluated under different scenarios and with its results compared to some state-of-the-art techniques. Its overall performance presented suitable results that are comparable to the other techniques, thus showing to be a promising tool to the problem of feature selection.en
dc.description.affiliationUNESP - São Paulo State University School of Sciences
dc.description.affiliationUNESP - São Paulo State University School of Engineering
dc.description.affiliationUFSCar - Federal University of São Carlos Department of Computing
dc.description.affiliationUnespUNESP - São Paulo State University School of Sciences
dc.description.affiliationUnespUNESP - São Paulo State University School of Engineering
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: #2013/07375-0
dc.description.sponsorshipIdFAPESP: #2013/08645-0
dc.description.sponsorshipIdFAPESP: #2014/12236-1
dc.description.sponsorshipIdFAPESP: #2016/19403-6
dc.description.sponsorshipIdFAPESP: #2017/02286-0
dc.description.sponsorshipIdFAPESP: #2017/22905-6
dc.description.sponsorshipIdCNPq: #306166/2014-3
dc.description.sponsorshipIdCNPq: #307066/2017-7
dc.description.sponsorshipIdCNPq: #308194/2017-9
dc.format.extent468-481
dc.identifierhttp://dx.doi.org/10.1016/j.compeleceng.2018.10.013
dc.identifier.citationComputers and Electrical Engineering, v. 72, p. 468-481.
dc.identifier.doi10.1016/j.compeleceng.2018.10.013
dc.identifier.issn0045-7906
dc.identifier.scopus2-s2.0-85055318690
dc.identifier.urihttp://hdl.handle.net/11449/189832
dc.language.isoeng
dc.relation.ispartofComputers and Electrical Engineering
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectBrain storm optimization
dc.subjectFeature selection
dc.subjectOptimum-Path forest
dc.titleFeature selection through binary brain storm optimizationen
dc.typeArtigo
unesp.author.lattes8212775960494686[3]
unesp.author.orcid0000-0002-6494-7514[1]
unesp.author.orcid0000-0002-8617-5404[3]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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