Artificial Bee Colony Algorithm for Feature Selection in Fraud Detection Process

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorFurlanetto, Gabriel Covello-
Autor(es): dc.creatorGomes, Vitoria Zanon-
Autor(es): dc.creatorBreve, Fabricio Aparecido-
Data de aceite: dc.date.accessioned2025-08-21T18:22:47Z-
Data de disponibilização: dc.date.available2025-08-21T18:22:47Z-
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/10.1007/978-3-031-36805-9_35-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/300212-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/300212-
Descrição: dc.descriptionMore and more, nowadays, better performance and quality of current classifiers are required when the topic is fraud detection. In this context, processes such as feature selection help to increase the quality of the results obtained by the existing classifiers in the literature, since the high dimensionality of current datasets and redundant information significantly affect the performance of these techniques. This work proposes a wrapper method of feature selection using the ABC algorithm combined with Logistic Regression classification, seeking to obtain better results for fraud detection. Through the tests performed and the results obtained, it is observed that the reduction in the number of features can reduce the database complexity and achieve a higher accuracy in classification when compared to the set classification when using all its attributes. It is also notable the effectiveness of the method as it reaches the proposed objective with as much as quality as other well-known methods while also contributing to optimizing parameters of other feature selection algorithms.-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computer Science Universidade Estadual Paulista (UNESP), Avenida 24A, 1515 - Jardim Bela Vista-
Descrição: dc.descriptionDepartment of Computer Science and Statistics Universidade Estadual Paulista (UNESP), Rua Cristóvão Colombo, 2265 - Jardim Nazareth-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computer Science Universidade Estadual Paulista (UNESP), Avenida 24A, 1515 - Jardim Bela Vista-
Descrição: dc.descriptionDepartment of Computer Science and Statistics Universidade Estadual Paulista (UNESP), Rua Cristóvão Colombo, 2265 - Jardim Nazareth-
Formato: dc.format535-549-
Idioma: dc.languageen-
Relação: dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectArtificial Bee Colony-
Palavras-chave: dc.subjectFeature Selection-
Palavras-chave: dc.subjectFraud Detection-
Palavras-chave: dc.subjectMachine Learning-
Título: dc.titleArtificial Bee Colony Algorithm for Feature Selection in Fraud Detection Process-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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