Enterprise credit evaluation model based on cost sensitive XGBoost

ZHANG Tianhua; ZHANG Yi; XIE Xiaojin

doi:10.12299/jsues.21-0236

Volume 36 Issue 2

Jun. 2022

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ZHANG Tianhua, ZHANG Yi, XIE Xiaojin. Enterprise credit evaluation model based on cost sensitive XGBoost[J]. Journal of Shanghai University of Engineering Science, 2022, 36(2): 218-223. doi: 10.12299/jsues.21-0236

Citation:

ZHANG Tianhua, ZHANG Yi, XIE Xiaojin. Enterprise credit evaluation model based on cost sensitive XGBoost[J]. Journal of Shanghai University of Engineering Science, 2022, 36(2): 218-223. doi: 10.12299/jsues.21-0236

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Enterprise credit evaluation model based on cost sensitive XGBoost

doi: 10.12299/jsues.21-0236

School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2021-10-29
Available Online: 2022-11-16
Publish Date: 2022-06-30

Abstract

Abstract

In China, the number of enterprises with bad credit is much smaller than that of enterprises with good credit. The extreme imbalance of sample categories results in the traditional credit evaluation model unable to fully learn the characteristics of bad credit enterprises during training. In order to improve the accuracy of extreme gradient boosting (XGBoost) in unbalanced classification problems such as enterprise credit evaluation, an enterprise credit evaluation model based on cost sensitive XGBoost was proposed. In the process of XGBoost algorithm fitting, the cost sensitive loss function was added to force the model to pay more attention to the characteristics of minority classes, and the bayesian optimization was introduced to adjust the hyperparameters of the model. Taking the datas of small and medium-sized enterprises in China's A-share market from 2016 to 2020 as the sample, the experimental results show that the enterprise credit evaluation model based on cost sensitive XGBoost can improve the identification accuracy of bad credit enterprises while ensuring the overall identification accuracy.
- loss function,
- extreme gradient boosting (XGBoost),
- cost sensitivity,
- enterprise credit evaluation

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References(16)

References

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