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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Feature selection through binary brain storm optimization

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Author(s):
Papa, Joao P. [1] ; Rosa, Gustavo H. [1] ; de Souza, Andre N. [2] ; Afonso, Luis C. S. [3]
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