Learning from imbalanced data sets with weighted cross-entropy function

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorAurélio, Yuri Sousa-
Autor(es): dc.creatorAlmeida, Gustavo Matheus de-
Autor(es): dc.creatorCastro, Cristiano Leite de-
Autor(es): dc.creatorBraga, Antônio Pádua-
Data de aceite: dc.date.accessioned2026-02-09T12:51:07Z-
Data de disponibilização: dc.date.available2026-02-09T12:51:07Z-
Data de envio: dc.date.issued2020-04-17-
Data de envio: dc.date.issued2020-04-17-
Data de envio: dc.date.issued2019-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/40165-
Fonte completa do material: dc.identifierhttps://link.springer.com/article/10.1007/s11063-018-09977-1-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1169519-
Descrição: dc.descriptionThis paper presents a novel approach to deal with the imbalanced data set problem in neural networks by incorporating prior probabilities into a cost-sensitive cross-entropy error function. Several classical benchmarks were tested for performance evaluation using different metrics, namely G-Mean, area under the ROC curve (AUC), adjusted G-Mean, Accuracy, True Positive Rate, True Negative Rate and F1-score. The obtained results were compared to well-known algorithms and showed the effectiveness and robustness of the proposed approach, which results in well-balanced classifiers given different imbalance scenarios.-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceNeural Processing Letters-
Título: dc.titleLearning from imbalanced data sets with weighted cross-entropy function-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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