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dc.creatorAurélio, Yuri Sousa-
dc.creatorAlmeida, Gustavo Matheus de-
dc.creatorCastro, Cristiano Leite de-
dc.creatorBraga, Antônio Pádua-
dc.date.accessioned2020-04-17T19:03:02Z-
dc.date.available2020-04-17T19:03:02Z-
dc.date.issued2019-
dc.identifier.citationAURELIO, Y. S. et al. Learning from imbalanced data sets with weighted cross-entropy function. Neural Processing Letters, [S.l.], v. 50, p. 1937-1949, 2019.pt_BR
dc.identifier.urihttps://link.springer.com/article/10.1007/s11063-018-09977-1pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/40165-
dc.description.abstractThis 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.pt_BR
dc.languageen_USpt_BR
dc.publisherSpringerpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceNeural Processing Letterspt_BR
dc.titleLearning from imbalanced data sets with weighted cross-entropy functionpt_BR
dc.typeArtigopt_BR
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