Opposition-Based Jellyfish Search for Feature Selection

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorRodrigues, Douglas-
Autor(es): dc.creatorCosta, Kelton Augusto Pontara Da-
Autor(es): dc.creatorGastaldello, Danilo Sinkiti-
Autor(es): dc.creatorSouza, Andre Nunes-
Autor(es): dc.creatorPapa, Joao Paulo-
Data de aceite: dc.date.accessioned2025-08-21T23:44:06Z-
Data de disponibilização: dc.date.available2025-08-21T23:44:06Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/IWSSIP58668.2023.10180255-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/309798-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/309798-
Descrição: dc.descriptionJellyfish Search (JS) is a recently proposed meta-heuristic optimization algorithm that simulates the behavior of jellyfish searching for food in ocean currents. However, JS suffers from problems related to population diversity in the search space and low convergence rate. This work proposes a new algorithm called opposition-Based Jellyfish Search (OJS), which uses opposition-Based Learning to increase search space coverage and the balance between exploration and exploitation. The OJS is validated against large-scale benchmark optimization functions from the CEC'2013 competition and also against feature selection from six datasets related to fault identification in power transformers. The experimental results demonstrated an increase in the OJS convergence rate concerning the original JS version and a performance improvement, obtaining lower fitness values in the large-scale benchmark optimization functions. Concerning feature selection, OJS obtained better accuracies than JS, demonstrating its viability for identifying faults in power transformers.-
Descrição: dc.descriptionSão Paulo State University Departament of Computing, SP-
Descrição: dc.descriptionSão Paulo State University Departament of Electrical Engineering, SP-
Descrição: dc.descriptionSão Paulo State University Departament of Computing, SP-
Descrição: dc.descriptionSão Paulo State University Departament of Electrical Engineering, SP-
Idioma: dc.languageen-
Relação: dc.relationInternational Conference on Systems, Signals, and Image Processing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectMachine Learning-
Palavras-chave: dc.subjectMetaheuristic-
Palavras-chave: dc.subjectopposition-Based Learning-
Palavras-chave: dc.subjectOptimization-
Título: dc.titleOpposition-Based Jellyfish Search for Feature Selection-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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