Swarm intelligent tuning of one-class v-SVM parameters

The problem of kernel parameters selection for one-class classifier, nu-SVM, is studied. An improved constrained particle swarm optimization (PSO) is proposed to optimize the RBF kernel parameters of the nu-SVM and two kinds of flexible RBF kernels are introduced. As a general purpose swarm intelligent and global optimization tool, PSO do not need the classifier performance criterion to be differentiable and convex. In order to handle the parameter constraints involved by the nu-SVM, the improved constrained PSO utilizes the punishment term to provide the constraints violation information. Application studies on an artificial banana dataset the efficiency of the proposed method.