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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, Jianxin1,2, WU, Xiong1,2, YU, Lean1,2,3 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]
Copyright of Technological & Economic Development of Economy is the property of Vilnius Gediminas Technical University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Technological+%26+Economic+Development+of+Economy%22">Technological & Economic Development of Economy</searchLink>. 2025, Vol. 31 Issue 6, p1687-1719. 33p.
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  Data: <searchLink fieldCode="DE" term="%22Credit+analysis%22">Credit analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink>
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  Data: 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]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Technological & Economic Development of Economy is the property of Vilnius Gediminas Technical University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3846/tede.2025.23060
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      – Code: eng
        Text: English
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        PageCount: 33
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      – SubjectFull: Credit analysis
        Type: general
      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Cluster analysis (Statistics)
        Type: general
      – SubjectFull: Ensemble learning
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      – SubjectFull: Boosting algorithms
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      – TitleFull: A HYBRID CLUSTERING AND BOOSTING TREE FEATURE SELECTION (CBTFS) METHOD FOR CREDIT RISK ASSESSMENT WITH HIGH-DIMENSIONALITY.
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              M: 11
              Text: 2025
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