Resumen
Credit scoring is an effective tool for banks and lending companies to manage the potential credit risk of borrowers. Machine learning algorithms have made grand progress in automatic and accurate discrimination of good and bad borrowers. Notably, ensemble approaches are a group of powerful tools to enhance the performance of credit scoring. Random forest (RF) and Gradient Boosting Decision Tree (GBDT) have become the mainstream ensemble methods for precise credit scoring. RF is a Bagging-based ensemble that realizes accurate credit scoring enriches the diversity base learners by modifying the training object. However, the optimization pattern that works on invariant training targets may increase the statistical independence of base learners. GBDT is a boosting-based ensemble approach that reduces the credit scoring error by iteratively changing the training target while keeping the training features unchanged. This may harm the diversity of base learners. In this study, we incorporate the advantages of the Bagging ensemble training strategy and boosting ensemble optimization pattern to enhance the diversity of base learners. An extreme learning machine-based supervised augmented GBDT is proposed to enhance the discriminative ability for credit scoring. Experimental results on 4 public credit datasets show a significant improvement in credit scoring and suggest that the proposed method is a good solution to realize accurate credit scoring.