Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. The ACO Algorithm
 3. An Improved Ant Colony Optimization Algorithm
 4. Support Vector Machine
 5. Parameter Optimization of SVM Based on Improved ACO
 6. Simulation Experiment
  6.1. Data Source
  6.2. Experimental Environment and Parameters
  6.3. Experimental Results and Analysis
 7. Conclusion
 References

키워드

저자

• Wen Chen [ School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, 611731 China, Sichuan University of Arts and Science, Dazhou , 635000 China ]
• Yixiang Tian [ School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, 611731 China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605