Credit scoring model based on a novel group feature selection method: The case of Chinese small-sized manufacturing enterprises-Reference-Cited by-同舟云学术

Credit scoring model based on a novel group feature selection method: The case of Chinese small-sized manufacturing enterprises

Published:2021-02-13 Issue:1 Volume:73 Page:122-138
ISSN:0160-5682
Container-title:Journal of the Operational Research Society
language:en
Short-container-title:Journal of the Operational Research Society

Author:

Zhang Zhipeng¹,Chi Guotai¹,Colombage Sisira²,Zhou Ying¹

Affiliation:

1. Dalian University of Technology, Dalian City, China

2. Federation University Australia, Ballarat, Australia

Funder

the Key Programs of National Natural Science Foundation of China

the General Programs of National Natural Science Foundation of China

the Youth Programs of National Natural Science Foundation of China

the Major Projects of National Social Science Foundation of China

Chinese Scholarship Council

Publisher

Informa UK Limited

Subject

Marketing,Management Science and Operations Research,Strategy and Management,Management Information Systems

Link

https://www.tandfonline.com/doi/pdf/10.1080/01605682.2021.1880295

Reference26 articles.

1. Basel Committee on Banking Supervision. (2005). Studies on the validation of internal rating systems. Working Paper. No. 14. Basel Committee on Banking Supervision.

2. Integer programming models for feature selection: New extensions and a randomized solution algorithm

3. Classifying credit ratings for Asian banks using integrating feature selection and the CPDA-based rough sets approach

4. Combination of feature selection approaches with SVM in credit scoring

5. The Study of Credit Scoring Model Based on Group Lasso

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Advancing credit risk modelling with Machine Learning: A comprehensive review of the state-of-the-art;Engineering Applications of Artificial Intelligence;2024-11

2. Growth potential of machine learning in credit risk predicting of farmers in the industry 4.0 era;International Journal of Finance & Economics;2024-05-30

3. Enhancing Credit Scoring Models: Unveiling the Impact of Data Preprocessing and Feature Selection Techniques: Case Study based on Microfinancial institution in Bosnia and Herzegovina;2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH);2024-03-20

4. Default prediction based on a locally weighted dynamic ensemble model for imbalanced data;The Journal of Risk Model Validation;2024

5. Financial distress prediction with optimal decision trees based on the optimal sampling probability;The Journal of Risk Model Validation;2024