A new hybrid credit scoring ensemble model with feature enhancement and soft voting weight optimization

Abstract

The explosive development of artificial intelligence (AI) has reshaped all aspects of life, including credit scoring. At the same time, the rapid expansion of the consumer finance industry has led to a huge demand. In this study, a new hybrid ensemble model with feature enhancement and soft voting weight optimization is proposed to achieve superior predictive power for credit scoring. For mining and characterizing the implicit information of the features, a new voting-based feature enhancement method is proposed to adaptively integrate the outlier detection and clustering capabilities through the weighted voting mechanism to form a feature-enhanced training set. To balance the feature-enhanced training set precisely and effectively, a new bagging-based undersampling method is proposed to obtain a balanced training set by undersampling from the negative binomial distribution through the bagging strategy. To maximize the performance of the model, a new weight-optimized soft voting method is proposed to optimize the soft voting weights of the base classifiers in the classifier ensemble using the COBYLA algorithm and then constructing the stacking-based ensemble model. Five datasets and five evaluation indicators were used for evaluation. The experimental results demonstrate the superior performance of the proposed model and prove its robustness and effectiveness.