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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.)

A multi-objective artificial butterfly optimization approach for feature selection

Full text
Author(s):
Rodrigues, Douglas [1] ; de Albuquerque, Victor Hugo C. [2] ; Papa, Joao Paulo [1]
Total Authors: 3
Affiliation:
[1] UNESP Sao Paulo State Univ, Dept Comp, Bauru, SP - Brazil
[2] Univ Fortaleza, Grad Program Appl Informat, Fortaleza, Ceara - Brazil
Total Affiliations: 2
Document type: Journal article
Source: APPLIED SOFT COMPUTING; v. 94, SEP 2020.
Web of Science Citations: 0
Abstract

Feature selection plays an essential role in machine learning since high dimensional real-world datasets are becoming more popular nowadays. The very basic idea consists in selecting a compact but representative set of features that reduce the computational cost and minimize the classification error. In this paper, the authors propose single, multi- and many-objective binary versions of the Artificial Butterfly Optimization (ABO) in the context of feature selection. The authors also propose two different approaches: (i) the first one (MO-I) aims at optimizing the classification accuracy of each class individually, while (ii) the second one (MO-II) considers the feature set minimization in the process either. The experiments were conducted over eight public datasets, and the proposed approaches are compared against the well-known Particle Swarm Optimization, Firefly Algorithm, Flower Pollination Algorithm, Brainstorm Optimization, and the Black Hole Algorithm. The results showed that the binary single-objective ABO performed better than the other meta-heuristic techniques, selecting fewer features and also figuring a lower computational burden. Concerning multi- and many-objective feature selection, both MO-I and MO-II approaches performed better than their single-objective meta-heuristic counterparts. (C) 2020 Elsevier B.V. All rights reserved. (AU)

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 Opportunities: Research Projects - Thematic Grants
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: 17/02286-0 - Probabilistic models for commercial losses detection
Grantee:André Nunes de Souza
Support Opportunities: Regular Research Grants
FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
Grantee:João Paulo Papa
Support Opportunities: Regular Research Grants