A HYBRID CLUSTERING AND BOOSTING TREE FEATURE SELECTION (CBTFS) METHOD FOR CREDIT RISK ASSESSMENT WITH HIGH-DIMENSIONALITY.

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Title:	A HYBRID CLUSTERING AND BOOSTING TREE FEATURE SELECTION (CBTFS) METHOD FOR CREDIT RISK ASSESSMENT WITH HIGH-DIMENSIONALITY.
Authors:	ZHU, Jianxin^1,2, WU, Xiong^1,2, YU, Lean^1,2,3 yulean@amss.ac.cn, ZHANG, Xiaoming⁴ zhxmdy@163.com
Source:	Technological & Economic Development of Economy. 2025, Vol. 31 Issue 6, p1687-1719. 33p.
Subjects:	Credit analysis, Feature selection, Random forest algorithms, Cluster analysis (Statistics), Ensemble learning, Boosting algorithms
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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
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. [ABSTRACT FROM AUTHOR]
ISSN:	20294913
DOI:	10.3846/tede.2025.23060