A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data

Abstract

Credit risk assessment is often accompanied with sampling data imbalance. For this reason, this paper tries to propose a deep belief network (DBN) based resampling support vector machine (SVM) ensemble learning paradigm to solve imbalanced data problem in credit classification. In this paradigm, a bagging algorithm is first used to generate variable training subsets to make the subsets rebalanced and suitable in size. Then the SVM model is used as individual base classifier to formulate diverse ensemble input members. Finally, the DBN model is applied as an ensemble method to fuse the input members to aggregate the classification results. In addition, the weights of different classes are changed by introducing a revenue matrix in terms of revenue-sensitive technique, which helps to make the results more reasonable. The experimental results indicate that the classification performance are improved effectively when the DBN-based ensemble strategy is integrated with re-sampling techniques, especially in imbalanced-data problem, implying that the proposed DBN-based resampling SVM ensemble learning paradigm can be used as a promising tool for credit risk classification with imbalanced data.