Application of Rough Sets and LS-SVM to Credit Risk Assessment
Credit risk is the most important reason that led to failures of a modern commercial bank, and the most important key is credit risk evaluation that in the implementation of various aspects of credit risk management. The most critical, the credit risk of commercial banks to make an objective estimate of the state is necessary. A Least Squares Support Vector Machine (LS-SVM) classification model based on rough sets algorithm (RS-LS-SVM) was advanced, which adapted RS to extract principal components to replace the original indexes, so that the processing speed and classification accuracy can be improved. Then credit risk assessment example that apply this classification model was provided and compared with the method of SVM and BP neural networks, which shows the better performance and better classification accuracy of RS-LS-SVM.
credit risk assessment rough sets least squares support vector machine
Zhenzhen Yuan Jiajia Deng Xiue Yuan Yuansheng Huang
Department of Economics and Management North China Electric Power University Baoding, China
国际会议
太原
英文
656-659
2011-02-26（万方平台首次上网日期，不代表论文的发表时间）