What is the importance of selecting features for non-technical losses identification?

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dc.contributor.authorRamos, Caio C. O.
dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.authorSouza, André N. [UNESP]
dc.contributor.authorChiachia, Giovani
dc.contributor.authorFalcão, Alexandre X.
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.date.accessioned2014-05-27T11:25:57Z
dc.date.available2014-05-27T11:25:57Z
dc.date.issued2011-08-02
dc.description.abstractAlthough non-technical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy has not attracted much attention in this context. In this paper, we focus on this problem applying a novel feature selection algorithm based on Particle Swarm Optimization and Optimum-Path Forest. The results demonstrated that this method can improve the classification accuracy of possible frauds up to 49% in some datasets composed by industrial and commercial profiles. © 2011 IEEE.en
dc.description.affiliationDepartment of Electrical Engineering USP - University of São Paulo, São Paulo
dc.description.affiliationDepartment of Computing UNESP - São Paulo State University, Bauru, São Paulo
dc.description.affiliationDepartment of Electrical Engineering UNESP - São Paulo State University, Bauru, São Paulo
dc.description.affiliationInstitute of Computing University of Campinas, São Paulo
dc.description.affiliationUnespDepartment of Computing UNESP - São Paulo State University, Bauru, São Paulo
dc.description.affiliationUnespDepartment of Electrical Engineering UNESP - São Paulo State University, Bauru, São Paulo
dc.format.extent1045-1048
dc.identifierhttp://dx.doi.org/10.1109/ISCAS.2011.5937748
dc.identifier.citationProceedings - IEEE International Symposium on Circuits and Systems, p. 1045-1048.
dc.identifier.doi10.1109/ISCAS.2011.5937748
dc.identifier.issn0271-4310
dc.identifier.lattes8212775960494686
dc.identifier.scopus2-s2.0-79960865826
dc.identifier.urihttp://hdl.handle.net/11449/72586
dc.language.isoeng
dc.relation.ispartofProceedings - IEEE International Symposium on Circuits and Systems
dc.relation.ispartofsjr0,237
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectAutomatic identification
dc.subjectClassification accuracy
dc.subjectData sets
dc.subjectFeature selection algorithm
dc.subjectIdentification accuracy
dc.subjectNon-technical loss
dc.subjectAutomation
dc.subjectClassification (of information)
dc.subjectParticle swarm optimization (PSO)
dc.subjectFeature extraction
dc.titleWhat is the importance of selecting features for non-technical losses identification?en
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
unesp.author.lattes8212775960494686[3]
unesp.author.orcid0000-0002-8617-5404[3]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Engenharia, Baurupt
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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Bauru - FC - Faculdade de Ciências
Artigos - Engenharia Elétrica - FEB