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

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

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 sele...

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Bibliographic Details
Main Authors: Jianxin Zhu, Xiong Wu, Lean Yu, Xiaoming Zhang
Format: Article
Language:English
Published: Vilnius Gediminas Technical University 2025-02-01
Series:Technological and Economic Development of Economy
Subjects:
feature selection
high-dimensionality
credit risk
minimum spanning tree
Online Access:https://jau.vgtu.lt/index.php/TEDE/article/view/23060
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https://jau.vgtu.lt/index.php/TEDE/article/view/23060

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