Research Article
An Explainable Enterprise Credit Evaluation Method Based on Logistic Regression Integration
@INPROCEEDINGS{10.4108/eai.27-10-2023.2341908, author={Bin Zhai and Fanyu Wang and Zhenping Xie and She Song}, title={An Explainable Enterprise Credit Evaluation Method Based on Logistic Regression Integration}, proceedings={Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27--29, 2023, Tianjin, China}, publisher={EAI}, proceedings_a={ICEMBDA}, year={2024}, month={1}, keywords={enterprise credit evaluation; interpretability; ensemble learning; logistic regression; xgboost}, doi={10.4108/eai.27-10-2023.2341908} }- Bin Zhai
Fanyu Wang
Zhenping Xie
She Song
Year: 2024
An Explainable Enterprise Credit Evaluation Method Based on Logistic Regression Integration
ICEMBDA
EAI
DOI: 10.4108/eai.27-10-2023.2341908
Abstract
Enterprise credit evaluation serves as a necessary process for the construction of social credit economic system. With the increasing requirements for intelligence and efficiency of credit evaluation, machine learning methods are widely employed in credit evaluation. However, the weak interpretability and low data transparency of the current deep neural structure adversely affect the trust between humans and black-box enterprise credit evaluation methods. By revisiting the interpretability in integrated learning, we proposed an interpretable enterprise credit evaluation method with a non-repetitively tree-splitting process.
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