Application of RBF neural network optimal segmentation algorithm in credit rating

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 mod...

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Veröffentlicht in:Neural computing & applications Jg. 33; H. 14; S. 8227 - 8235
Hauptverfasser: Li, Xuetao, Sun, Yi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.07.2021
Springer Nature B.V
Schlagworte:
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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-04958-9