Learning from Imbalanced Data Sets with Weighted Cross-Entropy Function

• Published in: Neural processing letters Vol. 50; no. 2; pp. 1937 - 1949
• Main Authors: Aurelio, Yuri Sousa, de Almeida, Gustavo Matheus, de Castro, Cristiano Leite, Braga, Antonio Padua
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2019; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Classification; Complex Systems; Computational Intelligence; Computer Science; Datasets; Decision theory; Entropy; Entropy (Information theory); Error functions; Neural networks; Performance evaluation; Probability; Classification problem; Imbalanced data; Multilayer perceptron; Back-propagation; Cost-sensitive function

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, ar...