Social-Spider Optimization-based Support Vector Machines applied for energy theft detection

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dc.contributor.authorPereira, Danillo R.
dc.contributor.authorPazoti, Mario A.
dc.contributor.authorPereira, Luis A. M.
dc.contributor.authorRodrigues, Douglas
dc.contributor.authorRamos, Caio O. [UNESP]
dc.contributor.authorSouza, Andre N. [UNESP]
dc.contributor.authorPapa, Joao P. [UNESP]
dc.contributor.institutionUniv Western Sao Paulo
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-11-27T07:08:26Z
dc.date.available2018-11-27T07:08:26Z
dc.date.issued2016-01-01
dc.description.abstractThe problem of Support Vector Machines (SVM) tuning parameters (i.e., model selection) has been paramount in the last years, mainly because of the high computational burden for SVM training step. In this paper, we address this problem by introducing a recently developed evolutionary-based algorithm called Social-Spider Optimization (SSO), as well as we introduce SSO for feature selection purposes. The model selection task has been handled in three distinct scenarios: (i) feature selection, (ii) tuning parameters and (iii) feature selection+tuning parameters. Such extensive set of experiments against with some state-of-the-art evolutionary optimization techniques (i.e., Particle Swarm Optimization and Novel Global-best Harmony Search) demonstrated SSO is a suitable approach for SVM model selection, since it obtained the top results in 8 out 10 datasets employed in this work (considering all three scenarios). Notice the best scenario seemed to be the combination of both feature selection and SVM tuning parameters. In addition, we validated the proposed approach in the context of theft detection in power distribution systems. (C) 2015 Elsevier Ltd. All rights reserved.en
dc.description.affiliationUniv Western Sao Paulo, Informat Fac Presidente Prudente, Presidente Prudente, SP, Brazil
dc.description.affiliationUniv Estadual Campinas, Inst Comp, Campinas, SP, Brazil
dc.description.affiliationUniv Fed Sao Carlos, Dept Comp Sci, BR-13560 Sao Carlos, SP, Brazil
dc.description.affiliationSao Paulo State Univ, Dept Elect Engn, Sao Paulo, Brazil
dc.description.affiliationSao Paulo State Univ, Dept Comp, Sao Paulo, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Dept Elect Engn, Sao Paulo, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Dept Comp, Sao Paulo, Brazil
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: 2012/06472-9
dc.description.sponsorshipIdFAPESP: 2013/20387-7
dc.description.sponsorshipIdFAPESP: 2014/16250-9
dc.description.sponsorshipIdCNPq: 303182/2011-3
dc.description.sponsorshipIdCNPq: 470571/2013-6
dc.description.sponsorshipIdCNPq: 306166/2014-3
dc.format.extent25-38
dc.identifierhttp://dx.doi.org/10.1016/j.compeleceng.2015.11.001
dc.identifier.citationComputers & Electrical Engineering. Oxford: Pergamon-elsevier Science Ltd, v. 49, p. 25-38, 2016.
dc.identifier.doi10.1016/j.compeleceng.2015.11.001
dc.identifier.fileWOS000368208100003.pdf
dc.identifier.issn0045-7906
dc.identifier.urihttp://hdl.handle.net/11449/165037
dc.identifier.wosWOS:000368208100003
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofComputers & Electrical Engineering
dc.relation.ispartofsjr0,401
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectNontechnical losses
dc.subjectPower distribution systems
dc.subjectSocial-Spider Optimization
dc.subjectSupport Vector Machines
dc.titleSocial-Spider Optimization-based Support Vector Machines applied for energy theft detectionen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
unesp.author.lattes8212775960494686[6]
unesp.author.orcid0000-0002-8617-5404[6]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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