基于粒子群算法的Universum SVM参数选择

分类器的模型参数对分类结果有直接影响.针对引入无关样本的Universum SVM算法中模型参数选择问题,采用粒子群优化(particle swarm optimization,PSO)算法对其进行优化.该方法概念简单、计算效率高且受问题维数变化的影响较小,可实现对多个参数同时优选.此外,在PSO中粒子适应度函数的选择是一个关键问题.考虑k遍交叉验证法的估计无偏性,利用交叉验证误差作为评价粒子优劣的适应值.通过舌象样本数据实验,对参数优选前后测试样本识别正确率进行比较,实验结果验证了该算法的有效性.

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.