Parameter optimization of SVM based on improved ACO for data classification

Abstract

The parameters of support vector machine have a great influence on the learning ability and generalization ability, so an improved ant colony optimization algorithm is proposed to optimize the parameters of SVM, then an optimized SVM classifier (IMACO-SVM) is proposed for data classification. In the IMACO-SVM, the adaptive adjustment pheromone strategy is used to make relatively uniform pheromone distribution and the improved pheromone updating method is used to submerge the heuristic factor by the residual pheromone information, in order to effectively solve the contradiction between expanding search and finding optimal solution. The selection of parameters of the SVM is regarded as a combination optimization of parameters in order to establish the objective function of combination optimization. The improved ACO algorithm with good robustness and positive feedback characteristics and parallel searching is used to search for the optimal value of objective function. In order to validate the classification effectiveness of the IMACO-SVM algorithm, some experimental data from the UCI machine learning database are selected in this paper. The classification results show that the proposed IMACO-SVM algorithm has higher classification ability and classification accuracy.