A multi-objective artificial butterfly optimization approach for feature selection

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...

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Detalles Bibliográficos
Autores: Rodrigues, Douglas [UNESP], de Albuquerque, Victor Hugo C., Papa, João Paulo [UNESP]
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:Brasil
Institución:Universidade Estadual Paulista (UNESP)
Repositorio:Repositório Institucional da UNESP
Idioma:inglés
OAI Identifier:oai:repositorio.unesp.br:11449/201828
Acceso en línea:http://dx.doi.org/10.1016/j.asoc.2020.106442
http://hdl.handle.net/11449/201828
Access Level:acceso abierto
Palabra clave:Machine learning
Many-objective optimization
Meta-heuristic algorithms
Pattern recognition
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spelling A multi-objective artificial butterfly optimization approach for feature selectionMachine learningMany-objective optimizationMeta-heuristic algorithmsPattern recognitionFeature 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.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Department of Computing UNESP - São Paulo State UniversityGraduate Program in Applied Informatics University of FortalezaDepartment of Computing UNESP - São Paulo State UniversityCAPES: #2014/12236-1CAPES: #2014/16250-9CAPES: #2016/19403-6CAPES: #2017/02286-0CNPq: #304315/2017-6CNPq: #306166/2014-3CNPq: #427968/2018-6CNPq: #430274/2018-1Universidade Estadual Paulista (Unesp)University of Fortaleza2020-12-12T02:42:52Z2020-12-12T02:42:52Z2020-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.asoc.2020.106442Applied Soft Computing Journal, v. 94.1568-4946http://hdl.handle.net/11449/20182810.1016/j.asoc.2020.1064422-s2.0-85085731896Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengApplied Soft Computing Journalinfo:eu-repo/semantics/openAccessRodrigues, Douglas [UNESP]de Albuquerque, Victor Hugo C.Papa, João Paulo [UNESP]2025-06-24T05:58:20Zoai:repositorio.unesp.br:11449/201828Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestrepositoriounesp@unesp.bropendoar:29462025-06-24T05:58:20Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv A multi-objective artificial butterfly optimization approach for feature selection
title A multi-objective artificial butterfly optimization approach for feature selection
spellingShingle A multi-objective artificial butterfly optimization approach for feature selection
Rodrigues, Douglas [UNESP]
Machine learning
Many-objective optimization
Meta-heuristic algorithms
Pattern recognition
title_short A multi-objective artificial butterfly optimization approach for feature selection
title_full A multi-objective artificial butterfly optimization approach for feature selection
title_fullStr A multi-objective artificial butterfly optimization approach for feature selection
title_full_unstemmed A multi-objective artificial butterfly optimization approach for feature selection
title_sort A multi-objective artificial butterfly optimization approach for feature selection
dc.creator.none.fl_str_mv Rodrigues, Douglas [UNESP]
de Albuquerque, Victor Hugo C.
Papa, João Paulo [UNESP]
author Rodrigues, Douglas [UNESP]
author_facet Rodrigues, Douglas [UNESP]
de Albuquerque, Victor Hugo C.
Papa, João Paulo [UNESP]
author_role author
author2 de Albuquerque, Victor Hugo C.
Papa, João Paulo [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
University of Fortaleza
dc.subject.por.fl_str_mv Machine learning
Many-objective optimization
Meta-heuristic algorithms
Pattern recognition
topic Machine learning
Many-objective optimization
Meta-heuristic algorithms
Pattern recognition
description 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.
publishDate 2020
dc.date.none.fl_str_mv 2020-12-12T02:42:52Z
2020-12-12T02:42:52Z
2020-09-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.asoc.2020.106442
Applied Soft Computing Journal, v. 94.
1568-4946
http://hdl.handle.net/11449/201828
10.1016/j.asoc.2020.106442
2-s2.0-85085731896
url http://dx.doi.org/10.1016/j.asoc.2020.106442
http://hdl.handle.net/11449/201828
identifier_str_mv Applied Soft Computing Journal, v. 94.
1568-4946
10.1016/j.asoc.2020.106442
2-s2.0-85085731896
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Applied Soft Computing Journal
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv repositoriounesp@unesp.br
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