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

dc.contributorUniversidade de São Paulo (USP)
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorUniversidade Estadual de Campinas (UNICAMP)
dc.date.accessioned2014-05-27T11:25:57Z
dc.date.available2014-05-27T11:25:57Z
dc.date.created2014-05-27T11:25:57Z
dc.date.issued2011-08-02
dc.identifierProceedings - IEEE International Symposium on Circuits and Systems, p. 1045-1048.
dc.identifier0271-4310
dc.identifierhttp://hdl.handle.net/11449/72586
dc.identifier10.1109/ISCAS.2011.5937748
dc.identifier2-s2.0-79960865826
dc.identifier8212775960494686
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.
dc.languageeng
dc.relationProceedings - IEEE International Symposium on Circuits and Systems
dc.relation0,237
dc.rightsAcesso 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?
dc.typeActas de congresos


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