Support Vector Machines Classification for High-Dimentional Dataset

Abstract

For improve classification accuracy, this paper discusses the problem of feature selection for high-dimensional data and SVM parameter optimization. An SVM classification system based on simulated annealing (SA) is proposed to improve the performance of the SVM classifier. The experiments are conducted on the basis of benchmark dataset. The obtained results confirm the superiority of the SA-SVM approach compared to default parameters SVM classifier, grid search SVM parameter approach and suggest that further substantial improvements in terms of classification accuracy can be achieved by the proposed SA-SVM classification technique.