A multi-objective artificial butterfly optimization approach for feature selection

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniversity of Fortaleza-
Autor(es): dc.creatorRodrigues, Douglas [UNESP]-
Autor(es): dc.creatorde Albuquerque, Victor Hugo C.-
Autor(es): dc.creatorPapa, João Paulo [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:34:37Z-
Data de disponibilização: dc.date.available2022-02-22T00:34:37Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-09-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.asoc.2020.106442-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/201828-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/201828-
Descrição: dc.descriptionFeature 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.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionDepartment of Computing UNESP - São Paulo State University-
Descrição: dc.descriptionGraduate Program in Applied Informatics University of Fortaleza-
Descrição: dc.descriptionDepartment of Computing UNESP - São Paulo State University-
Descrição: dc.descriptionCAPES: #2014/12236-1-
Descrição: dc.descriptionCAPES: #2014/16250-9-
Descrição: dc.descriptionCAPES: #2016/19403-6-
Descrição: dc.descriptionCAPES: #2017/02286-0-
Descrição: dc.descriptionCNPq: #304315/2017-6-
Descrição: dc.descriptionCNPq: #306166/2014-3-
Descrição: dc.descriptionCNPq: #427968/2018-6-
Descrição: dc.descriptionCNPq: #430274/2018-1-
Idioma: dc.languageen-
Relação: dc.relationApplied Soft Computing Journal-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectMany-objective optimization-
Palavras-chave: dc.subjectMeta-heuristic algorithms-
Palavras-chave: dc.subjectPattern recognition-
Título: dc.titleA multi-objective artificial butterfly optimization approach for feature selection-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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