Particle swarm optimization for parameter determination and feature selection of support vector machines

Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, without reducing SVM classification accuracy. A particle swarm optimization (PSO) based approach for parameter determination and feature selection of the SVM, termed PSO + SVM, is developed. Several public datasets are employed to calculate the classification accuracy rate in order to evaluate the developed PSO + SVM approach. The developed approach was compared with grid search, which is a conventional method of searching parameter values, and other approaches. Experimental results demonstrate that the classification accuracy rates of the developed approach surpass those of grid search and many other approaches, and that the developed PSO + SVM approach has a similar result to GA + SVM. Therefore, the PSO + SVM approach is valuable for parameter determination and feature selection in an SVM.