Paper Title:
Parameters Optimization in SVM Based-On Ant Colony Optimization Algorithm
  Abstract

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.

  Info
Periodical
Advanced Materials Research (Volumes 121-122)
Edited by
Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo
Pages
470-475
DOI
10.4028/www.scientific.net/AMR.121-122.470
Citation
X. Y. Liu, H. Y. Jiang, F. Z. Tang, "Parameters Optimization in SVM Based-On Ant Colony Optimization Algorithm", Advanced Materials Research, Vols. 121-122, pp. 470-475, 2010
Online since
June 2010
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Price
$32.00
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