The nature of statistical learning theory
The nature of statistical learning theory
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Sparse multikernel support vector regression machines trained by active learning
Expert Systems with Applications: An International Journal
Recurrent sparse support vector regression machines trained by active learning in the time-domain
Expert Systems with Applications: An International Journal
A dynamic model selection strategy for support vector machine classifiers
Applied Soft Computing
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The parameters of support vector machine (SVM) are crucial to the model's classification performance. Aiming at the randomicity of selecting the parameters in SVM, this paper constructed a PSO-SVM model by using particle swarm optimization (PSO) to search the parameters of SVM. The model was used for personal credit scoring in commercial banks and particles' fitness function was used to control the type II error which costs huger loss to commercial banks. Compared with BP NN, the application results indicate that PSO-SVM gets higher classification accuracy with lower type II error rate and the model shows stronger robustness, which presents more applicable for commercial banks to control personal credit risks.