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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/72586
Title: 
What is the importance of selecting features for non-technical losses identification?
Author(s): 
Institution: 
  • Universidade de São Paulo (USP)
  • Universidade Estadual Paulista (UNESP)
  • Universidade Estadual de Campinas (UNICAMP)
ISSN: 
0271-4310
Abstract: 
Although 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.
Issue Date: 
2-Aug-2011
Citation: 
Proceedings - IEEE International Symposium on Circuits and Systems, p. 1045-1048.
Time Duration: 
1045-1048
Keywords: 
  • Automatic identification
  • Classification accuracy
  • Data sets
  • Feature selection algorithm
  • Identification accuracy
  • Non-technical loss
  • Automation
  • Classification (of information)
  • Particle swarm optimization (PSO)
  • Feature extraction
Source: 
http://dx.doi.org/10.1109/ISCAS.2011.5937748
URI: 
Access Rights: 
Acesso restrito
Type: 
outro
Source:
http://repositorio.unesp.br/handle/11449/72586
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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