Affiliations: [a] Department of Mathematical Economics, Ho Chi Minh University of Banking, Vietnam
| [b] Faculty of Mathematics and Statistics, University of Economics Ho Chi Minh City, Vietnam
| [c] Department of Mathematics, International University, Vietnam National University, Ho Chi Minh City, Vietnam
Correspondence:
[*]
Corresponding author. Bui T.T. My. E-mail: [email protected].
Abstract: Credit scoring is a typical example of imbalanced classification, which poses a challenge to conventional machine learning algorithms and statistical classifiers when attempting to accurately predict outcomes for defaulting customers. In this paper, we propose a credit scoring classifier called Decision Tree Ensemble model (DTE). This model effectively addresses the challenge of imbalanced data and identifies significant features that influence the likelihood of credit status. An experiment demonstrates that DTE exhibits superior performance metrics in comparison to well-known based-tree ensemble classifiers such as Bagging, Random Forest, and AdaBoost, particularly when integrated with resampling techniques for handling imbalanced data.
Keywords: Classifiers, credit scoring, decision tree, ensemble classifiers, imbalanced data
