| Full text | |
| Author(s): |
Total Authors: 4
|
| Affiliation: | [1] UNESP Sao Paulo State Univ, Sch Sci, Bauru - Brazil
[2] UNESP Sao Paulo State Univ, Sch Engn, Bauru - Brazil
[3] UFSCAR Fed Univ Sao Carlos, Dept Comp, Sao Carlos, SP - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | COMPUTERS & ELECTRICAL ENGINEERING; v. 72, p. 468-481, NOV 2018. |
| Web of Science Citations: | 3 |
| Abstract | |
Feature selection stands for the process of finding the most relevant subset of features based on some criterion, which turns out to be an optimization task. In this context, several metaheuristic techniques have been extensively studied achieving results comparable to some state-of-the-art and traditional optimization techniques. This paper introduces a variation of the Brain Storm Optimization (i.e., Binary Brain Storm Optimization) for feature selection purposes, where real-valued solutions are mapped onto a boolean hyper cube using different transfer functions. The proposed Binary Brain Storm Optimization was evaluated under different scenarios and with its results compared to some state-of-the-art techniques. Its overall performance presented suitable results that are comparable to the other techniques, thus showing to be a promising tool to the problem of feature selection. (C) 2018 Elsevier Ltd. All rights reserved. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | José Alberto Cuminato |
| Support type: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 16/19403-6 - Energy-based learning models and their applications |
| Grantee: | João Paulo Papa |
| Support type: | Regular Research Grants |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support type: | Research Projects - Thematic Grants |
| FAPESP's process: | 13/08645-0 - Re-Ranking and rank aggregation approaches for image retrieval tasks |
| Grantee: | Daniel Carlos Guimarães Pedronette |
| Support type: | Research Grants - Young Investigators Grants |
| FAPESP's process: | 17/02286-0 - Probabilistic models for commercial losses detection |
| Grantee: | André Nunes de Souza |
| Support type: | Regular Research Grants |
| FAPESP's process: | 17/22905-6 - About image security using machine learning |
| Grantee: | Kelton Augusto Pontara da Costa |
| Support type: | Regular Research Grants |