Please use this identifier to cite or link to this item:
|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.|
gravitational search algorithm
particle swarm optimization
|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|
|Appears in Collections:||IC - Artigos e Outros Documentos|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.