Social-Spider Optimization-based Support Vector Machines applied for energy theft detection

Autor(es)

Pereira, Danillo R. ^[1] ; Pazoti, Mario A. ^[1] ; Pereira, Luis A. M. ^[2] ; Rodrigues, Douglas ^[3] ; Ramos, Caio O. ^[4] ; Souza, Andre N. ^[4] ; Papa, Joao P. ^[5]

Afiliação do(s) autor(es)

^[1] Univ Western Sao Paulo, Informat Fac Presidente Prudente, Presidente Prudente, SP - Brazil

^[2] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil

^[3] Univ Fed Sao Carlos, Dept Comp Sci, BR-13560 Sao Carlos, SP - Brazil

^[4] Sao Paulo State Univ, Dept Elect Engn, Sao Paulo - Brazil

^[5] Sao Paulo State Univ, Dept Comp, Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: COMPUTERS & ELECTRICAL ENGINEERING; v. 49, p. 25-38, JAN 2016.
• Citações Web of Science: 17

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. (AU)

• Processo FAPESP: 14/16250-9 - Sobre a otimização de parâmetros em técnicas de aprendizado de máquina: avanços e paradigmas
• Beneficiário: João Paulo Papa
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 12/06472-9 - Explorando abordagens de classificação contextual para Floresta de Caminhos Ótimos
• Beneficiário: Daniel Osaku
• Linha de fomento: Bolsas no Brasil - Doutorado
• Processo FAPESP: 13/20387-7 - Otimização de hiperparâmetros em arquiteturas de aprendizado em profundidade
• Beneficiário: João Paulo Papa
• Linha de fomento: Bolsas no Exterior - Pesquisa