Training cost-sensitive Deep Belief Networks on imbalance data problems

Many real-world problems are usually unbalanced, where datasets present skewed class distributions, such as failure diagnosis, spam detection, anomaly detection, fraud detection, oil spillage detection and medical diagnosis, etc. Deep Belief Network (DBN) is a competitive machine learning technique with good performance in many applications. However, some machine learning methods are likely to give poor performance with imbalanced data between classes since they assume equal costs for each class intrinsically. To deal with this problem, existing researches only focus on sampling based approaches and lack of studies about cost-sensitive based approaches. This paper proposes cost-sensitive Deep Belief Networks for such imbalanced classification problems. The proposed approach is extended to multi-class scenario. Unequalized misclassification costs between classes have been applied to DBN. Extensive comparison with extreme learning machines is provided as a proof of the ability of the proposed approach to perform competitively on imbalanced datasets. An evolutionary algorithm is also implemented to optimize the misclassification costs for each class in cost matrix.