Journal
NEURAL PROCESSING LETTERS
Volume 50, Issue 2, Pages 1937-1949Publisher
SPRINGER
DOI: 10.1007/s11063-018-09977-1
Keywords
Multilayer perceptron; Imbalanced data; Classification problem; Back-propagation; Cost-sensitive function
Categories
Funding
- CNPq
- FAPEMIG
- CAPES
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available