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Campo DC | Valor | Idioma |
---|---|---|
dc.creator | Aurélio, Yuri Sousa | - |
dc.creator | Almeida, Gustavo Matheus de | - |
dc.creator | Castro, Cristiano Leite de | - |
dc.creator | Braga, Antônio Pádua | - |
dc.date.accessioned | 2020-04-17T19:03:02Z | - |
dc.date.available | 2020-04-17T19:03:02Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | AURELIO, 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.uri | https://link.springer.com/article/10.1007/s11063-018-09977-1 | pt_BR |
dc.identifier.uri | http://repositorio.ufla.br/jspui/handle/1/40165 | - |
dc.description.abstract | This 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.language | en_US | pt_BR |
dc.publisher | Springer | pt_BR |
dc.rights | restrictAccess | pt_BR |
dc.source | Neural Processing Letters | pt_BR |
dc.title | Learning from imbalanced data sets with weighted cross-entropy function | pt_BR |
dc.type | Artigo | pt_BR |
Aparece nas coleções: | DCC - Artigos publicados em periódicos |
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