Combining Feature Selection and Classification Using LASSO-Based MCO Classifier for Credit Risk Evaluation-Reference-Cited by-同舟云学术

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Combining Feature Selection and Classification Using LASSO-Based MCO Classifier for Credit Risk Evaluation

Published:2024-01-08 Issue: Volume: Page:
ISSN:0927-7099
Container-title:Computational Economics
language:en
Short-container-title:Comput Econ

Author:

Li Xiufang,Zhang Zhiwang,Li Lingyun,Pan Hui

Funder

National Nature Science Foundation of China

Yantai School Land Integration Development Project

Major Program of Natural Science Foundation of the Higher Education Institutions of Jiangsu Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10614-023-10535-8.pdf

Reference50 articles.

1. Arora, N., & Kaur, P. D. (2019). A bolasso based consistent feature selection enabled random forest classification algorithm: An application to credit risk assessment. Applied Soft Computing, 86, 105936.

2. Bhattacharya, A., Biswas, S. K., & Mandal, A. (2022). Credit risk evaluation: A comprehensive study. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-022-13952-3

3. Bijak, K., & Thomas, L. C. (2012). Does segmentation always improve model performance in credit scoring? Expert Systems with Applications, 39(3), 2433–2442.

4. Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39(3), 3446–3453.

5. Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57, 203–216.

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