Social-Spider Optimization-based Support Vector Machines applied for energy theft detection
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Data
2016-01-01
Autores
Pereira, Danillo R.
Pazoti, Mario A.
Pereira, Luis A. M.
Rodrigues, Douglas
Ramos, Caio O. [UNESP]
Souza, Andre N. [UNESP]
Papa, Joao P. [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Elsevier B.V.
Resumo
The problem of Support Vector Machines (SVM) tuning parameters (i.e., model selection) has been paramount in the last years, mainly because of the high computational burden for SVM training step. In this paper, we address this problem by introducing a recently developed evolutionary-based algorithm called Social-Spider Optimization (SSO), as well as we introduce SSO for feature selection purposes. The model selection task has been handled in three distinct scenarios: (i) feature selection, (ii) tuning parameters and (iii) feature selection+tuning parameters. Such extensive set of experiments against with some state-of-the-art evolutionary optimization techniques (i.e., Particle Swarm Optimization and Novel Global-best Harmony Search) demonstrated SSO is a suitable approach for SVM model selection, since it obtained the top results in 8 out 10 datasets employed in this work (considering all three scenarios). Notice the best scenario seemed to be the combination of both feature selection and SVM tuning parameters. In addition, we validated the proposed approach in the context of theft detection in power distribution systems. (C) 2015 Elsevier Ltd. All rights reserved.
Descrição
Palavras-chave
Nontechnical losses, Power distribution systems, Social-Spider Optimization, Support Vector Machines
Como citar
Computers & Electrical Engineering. Oxford: Pergamon-elsevier Science Ltd, v. 49, p. 25-38, 2016.