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Type: Artigo
Title: New insights on nontechnical losses characterization through evolutionary-based feature selection
Author: Ramos, Caio César Oba
Souza, André Nunes de
Falcão, Alexandre Xavier
Papa, João 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: Seleção de características
Country: Estados Unidos
Editor: Institute of Electrical and Electronics Engineers
Rights: Fechado
Identifier DOI: 10.1109/TPWRD.2011.2170182
Date Issue: 2012
Appears in Collections:IC - Artigos e Outros Documentos

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