A hybrid clustering and boosting tree feature selection (CBTFS) method for credit risk assessment with high-dimensionality
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
Keyword : feature selection, high-dimensionality, credit risk, minimum spanning tree
How to Cite
Zhu, J., Wu, X., Yu, L., & Zhang, X. (2025). A hybrid clustering and boosting tree feature selection (CBTFS) method for credit risk assessment with high-dimensionality . Technological and Economic Development of Economy, 1-33. https://doi.org/10.3846/tede.2025.23060
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Niu, K., Zhang, Z., Liu, Y., & Li, R. (2020). Resampling ensemble model based on data distribution for imbalanced credit risk evaluation in P2P lending. Information Sciences, 536, 120–134. https://doi.org/10.1016/j.ins.2020.05.040
Norat, R., Wu, A. S., & Liu, X. (2023). Genetic algorithms with self-adaptation for predictive classification of Medicare standardized payments for physical therapists. Expert Systems with Applications, 218, Article 119529. https://doi.org/10.1016/j.eswa.2023.119529
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Palma-Mendoza, R. J., Rodriguez, D., & de-Marcos, L. (2018). Distributed relieff-based feature selection in Spark. Knowledge and Information Systems, 57(1), 1–20. https://doi.org/10.1007/s10115-017-1145-y
Pashaei, E., & Pashaei, E. (2022). Hybrid binary arithmetic optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical data. The Journal of Supercomputing, 78(13), 15598–15637. https://doi.org/10.1007/s11227-022-04507-2
Qian, H., Wang, B., Yuan, M., Gao, S., & Song, Y. (2022). Financial distress prediction using a corrected feature selection measure and gradient boosted decision tree. Expert Systems with Applications, 190, Article 116202. https://doi.org/10.1016/j.eswa.2021.116202
Rao, C., Liu, M., Goh, M., & Wen, J. (2020). 2-stage modified random forest model for credit risk assessment of P2P network lending to “Three Rurals” borrowers. Applied Soft Computing, 95, Article 106570. https://doi.org/10.1016/j.asoc.2020.106570
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Sahu, B., & Dash, S. (2024). Optimal feature selection from high-dimensional microarray dataset employing hybrid IG-Jaya model. Current Materials Science, 17(1), 21–43. https://doi.org/10.2174/2666145416666230124143912
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Sankhwar, S., Gupta, D., Ramya, K. C., Sheeba Rani, S., Shankar, K., & Lakshmanaprabu, S. K. (2020). Improved grey wolf optimization-based feature subset selection with fuzzy neural classifier for financial crisis prediction. Soft Computing, 24(1), 101–110. https://doi.org/10.1007/s00500-019-04323-6
Seijo-Pardo, B., Porto-Díaz, I., Bolón-Canedo, V., & Alonso-Betanzos, A. (2017). Ensemble feature selection: Homogeneous and heterogeneous approaches. Knowledge-Based Systems, 118, 124–139. https://doi.org/10.1016/j.knosys.2016.11.017
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