Application of Random Forest Classifier in Loan Default Forecast-Reference-Cited by-同舟云学术

Application of Random Forest Classifier in Loan Default Forecast

Published:2020 Issue: Volume: Page:410-420
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Zhang Huannan,Bi Yilin,Jiang Wangdong,Luo Chuntian,Cao Shengjia,Guo Peng,Zhang Jianjun

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-15-8101-4_37

Reference22 articles.

1. Torvekar, N., Game, P.S.: Predictive analysis of credit score for credit card defaulters. Int. J. Recent Technol. Eng. 7(1), 4 (2019)

2. Kurapati, N., Bhansali, P.K.: Predicting the credit defaulters using machine learning techniques. Int. J. Manag. Technol. Eng. 8(11), 6 (2018)

3. Jinwang, W., Zhouyi, G.: Customer credit risk assessment of commercial banks based on unbalanced samples – a case study of bank A. Finan. Theory Pract. 07, 51–57 (2018)

4. Zhao, J., Lu, H.: An over sampling random forest algorithm for unbalanced data classification. Comput. Appl. Softw. 36(04), 255–261+316 (2019)

5. Wei, Z.: Research on stochastic forest algorithm based on unbalanced data (2017)

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