Learning from Imbalanced Data Sets with Weighted Cross-Entropy Function
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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.
KeywordsMultilayer perceptron Imbalanced data Classification problem Back-propagation Cost-sensitive function
The authors would like to thank the funding agencies CNPq, FAPEMIG and CAPES for their financial support.
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