Earticle

초록

영어

Support vector machine is a machine learning method. It takes on the good generalization ability and prediction accuracy. But the parameters of SVM model seriously affect the generalization ability and prediction accuracy of SVM model on the great extent. So an improved particle swarm optimization (PSO) algorithm based on chaotic search is introduced into the SVM model in propose a novel data classification (AMPSVM) method for processing the complex data. The first, the ergodicity, stochastic property, and regularity of chaos is used to chaotically search the current best individual, which randomly replaces the selected individual in the population in order to speed up evolution, improve the searching ability, convergence speed and accuracy. Then the improved PSO algorithm is used to select and optimize the parameters of the SVM (AMPSVM) model in order to improve the learning performance and generalization ability of the SVM model. In order to verify the effectiveness of the AMPSVM method, UCI data is selected in here. The experiment results show that the proposed AMPSVM method takes on the strong generalization ability, best sensitivity and higher classification accuracy.

목차

Abstract
 1. Introduction
 2. Basic Methods
  2.1. Chaos
  2.2. Particle Swarm Optimization Algorithm
  2.3. Support Vector Machine(SVM)
 3. An Improved Particle Swarm Optimization (PSO) Algorithm
 4. A Novel Data Classification (AMPSVM) Method
 5. Experimental Results and Analysis
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Senhua Wang [ School of Economic and Managment, Beihang University, Beijing 100191 China ]
• Rui Li [ School of Business, Beijing Normal University, Beijing 100875 China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605