Model Selection of C-Support Vector Machines Based on Multi-Threading Genetic Algorithm

Since generalization performance of support vector machines depends a lot on parameter values of kernel functions, it is important to select optimal parameter values. How to finish optimal model selection of C-Support Vector Machines (C-SVM) with satisfiable speed is the main focus of this paper. We can hardly finish training process for large data sets with traditional methods because of long time-consuming cost. To take advantage of multi-threading and genetic algorithms, we studied a hybrid model selection method to select C and sigma of RBF kernel function for C-SVM classifier. This new method not only chooses global optimal parameters, but also saves training time based on parallel computing process. Experimental results show the efficiency and feasibility of the new method.

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