A Prediction Model for Credit Risk Measurement of Small and Micro Enterprises Based On Particle Swarm Optimization random forest algorithm

Authors

  • Feng Chai

DOI:

https://doi.org/10.62051/ijcsit.v3n1.29

Keywords:

Particle swarm optimization, Random forest algorithm, Credit risk, Predictive models

Abstract

In the context of rapid economic development, the credit risk assessment of small and micro enterprises has become the focus of attention in the financial field. The random forest algorithm is widely used in credit risk assessment due to its high accuracy and robustness, but it has some problems, such as difficulty in parameter selection and performance degradation in processing unbalanced datasets. In this study, we propose a credit risk prediction model for small and micro enterprises based on Particle Swarm Optimization Random Forest Algorithm (PSO-RF). The particle swarm optimization effectively solves the problem of random forest parameter selection and improves the prediction performance of the model through the balance of global search and local search. Experimental results show that the PSO-RF algorithm shows significant performance advantages in credit risk prediction, performs well in dealing with unbalanced datasets, and has certain advantages in feature selection. This study provides new ideas and methods for the credit risk assessment of small and micro enterprises, and is of great significance for improving the risk management capabilities of financial institutions and supporting the healthy development of small and micro enterprises.

References

Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.

Kennedy, J., & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks, 4, 1942-1948.

Zhao Jing, Zhang Wei, Li Li. (2017). Research on credit risk assessment model based on improved particle swarm optimization random forest. Computer Engineering and Applications, 53(5), 141-146.

Li Qiang, Zhang Xiaohui, Hu Xiaoming. (2018). Research on credit risk assessment model based on particle swarm optimization support vector machine. Computer Engineering and Applications, 54(6), 173-177.

Wang Fang, Li Li, Liu Jie. (2016). Research on credit risk assessment model based on particle swarm optimization neural network. Computer Engineering and Applications, 52(10), 174-179.

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Published

15-06-2024

Issue

Section

Articles

How to Cite

Chai, F. (2024). A Prediction Model for Credit Risk Measurement of Small and Micro Enterprises Based On Particle Swarm Optimization random forest algorithm. International Journal of Computer Science and Information Technology, 3(1), 227-234. https://doi.org/10.62051/ijcsit.v3n1.29

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