원문정보
초록
영어
Loan default evaluation and discrimination is a complicated issue because of its nonlinearity and uncertainty. Least square support vector machine (LS-SVM) has been successfully employed to solve regression and time series problem. This paper proposes a novel PSO-LS-SVM model based on the improved PSO algorithm to optimize parameters of LS-SVM, which is a new improved form by synthesizing the exiting model of PSO. Some evaluation indices, which are reduced without information loss by a genetic algorithm, are used to train PSO-LS-SVM and discriminate between healthy and default testing samples. A case study based on financial data acquired from listed companies has been carried out. Result has shown that the proposed model has a distinct improvement in the aspect of accuracy rate as compared to PSO-SVM, LS-SVM, SVM and BP neural network.
목차
1. Introduction
2. Methodology
2.1 Least Squares Support Vector Machines
2.2 Sample Attributes Reduction
2.3 The Improved Particle Swarm Optimization
2.4 Parameter Selection by the Improved PSO
3. Empirical Analysis
3.1 Attribute Reduction and Sample Data
3.2 Search the Best with Iteration
3.3 Empirical Results
4. Conclusion
Acknowledgements
References