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http://repositorio.ufla.br/jspui/handle/1/40165| Título: | Learning from imbalanced data sets with weighted cross-entropy function |
| Data do documento: | 2019 |
| Editor: | Springer |
| Citação: | 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. |
| Resumo: | 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. |
| URI: | https://link.springer.com/article/10.1007/s11063-018-09977-1 http://repositorio.ufla.br/jspui/handle/1/40165 |
| Aparece nas coleções: | DCC - Artigos publicados em periódicos |
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