Example dependent cost sensitive learning based selective deep ensemble model for customer credit scoring.

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

SUBMITTER: Xiao J 

PROVIDER: S-EPMC11836470 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature