Weighted ensemble based on differentiated sampling rates for imbalanced classification and application to credit risk assessment

Imbalanced data classification is an important research topic in machine learning. The class imbalance problem has a great impact on the classification performance of the algorithm. In this research direction, proposing an effective sampling strategy for imbalanced data is a challenging task. Although a lot of methods have been proposed to classify imbalanced data, the problem remains open. If a method reflects the data distribution and removes noisy samples, then good classification results will be obtained. Therefore, this paper proposes a weighted ensemble algorithm based on differentiated sampling rates (KSDE) and apply it to the field of credit risk assessment. KSDE removes noisy samples using the outlier detection technique. Then, multiple balanced training subsets are generated to train submodels using differentiated sampling rates. These training subsets sufficiently represent the distribution of data. Finally, the well-performing submodels are weighted and integrated to obtain the prediction result. We conducted comprehensive experiments to validate the performance of the proposed method. Comparing 12 state-of-the-art methods on 23 datasets. KSDE outperforms the recently proposed SPE (Self-paced Ensemble) by 12.46% in terms of TPR (True Positive Rate). In addition, KSDE achieves good results on 7 credit risk datasets. The experimental results show that the proposed method is competitive in solving the imbalanced data classification problem.