Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and <scp>AdaBoost</scp>-Reference-Cited by-同舟云学术

Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and AdaBoost

Published:2022-02-08 Issue:6 Volume:39 Page:
ISSN:0266-4720
Container-title:Expert Systems
language:en
Short-container-title:Expert Systems

Author:

Yao Gang¹^ORCID,Hu Xiaojian¹²,Zhou Taiyun³,Zhang Yue¹

Affiliation:

1. School of Management Hefei University of Technology Hefei China

2. Key Laboratory of Process Optimization and Intelligent Decision‐Making, Ministry of Education Hefei University of Technology Hefei China

3. School of Economics and Management Anhui Agricultural University Hefei China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12953

Reference64 articles.

1. Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks

2. FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY

3. Machine learning models and bankruptcy prediction

4. Financial Ratios As Predictors of Failure

5. The link between operational leanness and credit ratings

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