Abstract
In credit scoring, data often has class-imbalanced problems. However, traditional cost-sensitive learning methods rarely consider the varying costs among samples. Moreover, previous studies have limitations, such as the lack of fit to real-world business needs and limited model interpretability. To address these issues, this paper proposes a novel example-dependent cost-sensitive learning based selective deep ensemble (ECS-SDE) model for customer credit scoring, which integrates example-dependent cost-sensitive learning with the interpretable TabNet (attentive interpretable tabular learning) and GMDH (group method of data handling) deep neural networks. Specifically, we use TabNet, which excels in handling tabular data, as the base classifier and optimize its performance on imbalanced data with an example-dependent cost loss function. Next, we design a GMDH based on an example-dependent cost-sensitive symmetric criterion to selectively deep integrate the base classifiers. This approach reduces the redundancy of base models in traditional ensemble strategies and enhances classification performance. Experimental results show that the ECS-SDE model outperforms six cost-sensitive models and five advanced deep ensemble models in overall performance for credit scoring. It shows significant advantages in the BS(+), Save, and AUC metrics on four datasets. Furthermore, the ECS-SDE model provides strong interpretability, and detailed analysis reveals the key roles of various features in credit scoring.