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Type: Artigo de periódico
Title: New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection
Author: Oba Ramos, Caio Cesar
de Souza, Andre Nunes
Falcao, Alexandre Xavier
Papa, Joao Paulo
Abstract: Although nontechnical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy and to characterize possible illegal consumers has not attracted much attention in this context. In this paper, we focus on this problem by reviewing three evolutionary-based techniques for feature selection, and we also introduce one of them in this context. The results demonstrated that selecting the most representative features can improve a lot of the classification accuracy of possible frauds in datasets composed by industrial and commercial profiles.
Subject: Feature selection
gravitational search algorithm
harmony search
nontechnical losses
optimum-path forest
particle swarm optimization
pattern recognition
Editor: IEEE-Inst Electrical Electronics Engineers Inc
Citation: IEEE Transactions On Power delivery. IEEE-Inst Electrical Electronics Engineers Inc, v.27, n.1, p.140-146, 2012
Rights: fechado
Identifier DOI: 10.1109/TPWRD.2011.2170182
Date Issue: 2012
Appears in Collections:IC - Artigos e Outros Documentos

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