Application of RBF neural network optimal segmentation algorithm in credit rating

• Published in: Neural computing & applications Vol. 33; no. 14; pp. 8227 - 8235
• Main Authors: Li, Xuetao, Sun, Yi
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Credit ratings; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Neural networks; Outliers (statistics); Probability and Statistics in Computer Science; Radial basis function; Risk management; Robustness (mathematics); S. I : Intelligent Computing Methodologies in Machine learning for IoT Applications; Segmentation; Special Issue on Intelligent Computing Methodologies in Machine learning for IoT Applications; Classification data; Credit grading; Radial basis function neural network; Optimal segmentation method
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-020-04958-9

Credit rating is an important part of bank credit risk management. Since the traditional radial basis function network model is more susceptible to outliers and cannot effectively process the classification data, it is very sensitive in terms of the initial center and class width of the selected model. This paper mainly studies the application of the radial basis function neural network model combined with the optimal segmentation algorithm in the personal loan credit rating model of banks or other financial institutions. The optimal segmentation algorithm is improved and applied to the training of RBF neural network parameters in this paper to increase the center and width of the class, and the center and width of the RBF network model are further improved. Finally, the adaptive selection of the number of hidden nodes is realized by using the differential objective function of the class to adjust dynamically the structure of the radial basis function network model, which is used to establish the credit rating model. The experimental results show that the improved model has higher precision when dealing with non-numeric data, and the robustness of the improved model has been improved.