Parameters Optimization in SVM Based-On Ant Colony Optimization Algorithm
In this paper ACO (Ant Colony Optimization) algorithm, which is a well-known intelligent optimization method, is applied to selecting parameters for SVM.ACO has the characteristics of positive feedback, parallel mechanism and distributed computation. This paper gives comparison of ACO-SVM, PSO-SVM whose parameters are determined by particle swarm optimization algorithm, and traditional SVM whose parameters are decided through trial and error. The experimental results on real-world datasets show that this proposed method avoids randomness and subjectivity in the traditional SVM. Additionally it is able to gain better parameters which could dedicate to a higher classification accuracy than the PSO-SVM. Results confirm that proposed optimization method is better than the two others.
Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo
X. Y. Liu et al., "Parameters Optimization in SVM Based-On Ant Colony Optimization Algorithm", Advanced Materials Research, Vols. 121-122, pp. 470-475, 2010