| Full text | |
| Author(s): |
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]
Total Authors: 7
|
| Affiliation: | [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
Total Affiliations: 5
|
| Document type: | Journal article |
| Source: | COMPUTERS & ELECTRICAL ENGINEERING; v. 49, p. 25-38, JAN 2016. |
| Web of Science Citations: | 15 |
| Abstract | |
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) | |
| FAPESP's process: | 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 12/06472-9 - Exploring Contextual Classification Approaches for Optimum-Path Forest |
| Grantee: | Daniel Osaku |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 13/20387-7 - Hyperparameter optimization in deep learning arquitectures |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Scholarships abroad - Research |