Classification Based on Particle Swarm Optimization for Least Square Support Vector Machines Training

Classification problem is an important and complex problem in machine learning. Support vector machine (SVM) has recently emerged as a powerful technique for solving problems in classification, but its performance mainly depends on the parameters selection of it. Parameters selection for SVM is very complex in nature and quite hard to solve by conventional optimization techniques such as least Squares algorithm, for it has some limitations associated with overfitting, local optimum problems. So in this paper, Particle Swarm Optimization algorithm is proposed to choose the parameters of support vector machine (SVM) automatically in Classification problem. This method has been applied in Iris classification problem, the results show that the proposed approach has a better generalization performance and is also more accurate and effective than LS-SVM.