Earticle

초록

영어

Support vector machine(SVM) can effectively solve the classification problem with small samples, nonlinear and high dimensions, but it exits the weak generalization ability and low classification accuracy. So an improved genetic algorithm(IGA) is introduced in order to propose a new classification(IGASVM) method based on combining improved GA and SVM model. In the proposed IGASVM method, the self-adaptive control parameter strategy and improving convergence speed strategy are introduced into the GA to keep the diversity of the population, promptly reflect the premature convergence of the individual and escape from the local optimal solution for improving the search performance. Then the improved GA is used to optimize and determine the parameters of the SVM model in order to improve the learning ability and generalization ability of the SVM model for obtaining new classification (IGASVM) method. Finally, the experiment data is selected to test the effectiveness of the proposed IGASVM method. The experiment results show that the improved GA can effectively optimize and determine the parameters of the SVM model, and the IGASVM method takes on the better learning ability, generalization ability and classification accuracy.

목차

Abstract
 1. Introduction
 2. GA and SVM
  2.1. Genetic Algorithm
  2.2. Support Vector Machine(SVM)
 3. An Improved GA
  3.1. Self-Adaptive Control Parameter Strategy
  3.2. Improving Convergence Speed Strategy
 4. A New Classification(IGASVM) Method
 5. Experiment and Simulation Analysis
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Jing Huo [ School of Electronic Information and Electronical Engineering, Tianshui Normal University, Tianshui 741001, Gansu, China ]
• Yuxiang Zhao [ School of Electronic Information and Electronical Engineering, Tianshui Normal University, Tianshui 741001, Gansu, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605