Work

A hybrid clustering and boosting tree feature selection (CBTFS) method for credit risk assessment with high-dimensionality

Year: 2025

Type: article

Abstract: To solve the high-dimensional issue in credit risk assessment, a hybrid clustering and boosting tree feature selection method is proposed. In the hybrid methodology, an improved minimum spanning tree model is first used to remove redundant and irrelevant features. Then three embedded feature selection approaches (i.e., Random Forest, XGBoost, and AdaBoost) are used to further enhance the feature-ranking efficiency and obtain better prediction performance by applying the optimal features. For verification purpose, two real-world credit datasets are used to demonstrate the effectiveness of the proposed hybrid clustering and boosting tree feature selection (CBTFS) methodology. Experimental results demonstrated that the proposed method is superior to others classic feature selection methods. This indicates that the proposed hybrid clustering and boosting tree feature selection method can be used as a promising tool for solving high-dimensional issue in credit risk assessment. First published online 12 February 2025

Source: Technological and Economic Development of Economy

DOI: 10.3846/tede.2025.23060

Authors: Jianxin Zhu, Xiong Wu, Lean Yu, Xiaoming Zhang

Institutions: Harbin Engineering University, Ministry of Industry and Information Technology, Sichuan University, Jiangxi University of Finance and Economics

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $727

Funder: National Natural Science Foundation of China