A Transfer Learning Based Classifier Ensemble Model for Customer Credit Scoring

Abstract

Customer credit scoring is an important concern for numerous domestic and global industries. It is difficult to achieve satisfactory performance by traditional models constructed on the assumption that the training and test data are subject to the same distribution, because the customers usually come from different districts and may be subject to different distributions in reality. This study combines ensemble learning and transfer learning, and proposes a clustering and selecting based dynamic transfer ensemble (CSTE) model to transfer the related source domains to target domain for assisting in modeling. The experimental results in a large customer credit scoring dataset show that CSTE model outperforms two traditional credit scoring models, as well as three existing transfer learning models.