Model Parameter Selection of the Universum SVM Based on Particle Swarm Optimization

The model parameters of a classifier directly affect the classification results.According to the traits of additional irrelevant samples in the learning process of Universum SVM,this paper optimizes parameters with particle swarm optimization(PSO) due to its simple concept,high computational efficiency,and less impact by the changes of the problem dimension;therefore,several parameters can be simultaneously optimized.Besides,selection for fitness function is a key factor in PSO algorithm.According to its unbiased estimation,k-fold cross validation error is considered as the fitness value,by which an evaluation on the particle can be obtained.Finally,through experiment on tongue samples,the recognition accuracy rates on test samples before and after optimizing the parameters are compared.Result verifies the effectiveness of the proposed algorithm.