期刊
NEURAL PROCESSING LETTERS
卷 50, 期 2, 页码 1937-1949出版社
SPRINGER
DOI: 10.1007/s11063-018-09977-1
关键词
Multilayer perceptron; Imbalanced data; Classification problem; Back-propagation; Cost-sensitive function
资金
- CNPq
- FAPEMIG
- CAPES
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.
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