Earticle

초록

영어

In order to improve the performance of Support Vector Machine (SVM) classifier for imbalanced data, this paper proposes dynamic cost-sensitive SVM classifier based on chaos particle swarm optimization (CPDC_SVM). Firstly, this paper introduces dynamic cost-sensitive thought to SVM classifier, and gives the method for structuring dynamic cost and cost-sensitive SVM model. Secondly, we propose the evaluation methodology performance for classifier, and adopts decimal base to code the particles. At last, chaos thought is introduced in particle swarm optimization algorithm, and the Algorithm of the dynamic cost-sensitive SVM classifier is given, which improves convergent speed and accuracy of particle swarm optimization, and can optimize dynamic cost-sensitive SVM well, so CPDC_SVM adds effectively the convergence speed and accuracy for the particle swarm optimization algorithm. Experimental results show CPDC_SVM has higher precision than traditional SVM classifier, and dynamic cost and chaos particle swarm optimization can improve the performance for classifier.

목차

Abstract
 1. Introduce
 2. Dynamic Cost
 3. Cost-sensitive SVM
 4. Performance of classifier
 5. Dynamic cost-sensitive SVM Classifier Based on Chaos Particle Swarm Optimization Algorithm
  5.1. Chaotic Initialization
  5.2. Chaotic Disturbance
  5.3. Algorithm Description for Dynamic Cost-sensitive SVM Classifier
 6. Simulation Experiment
 7. Conclusion
 Acknowledgments
 References

키워드

저자

• Ruili Zhang [ Department of Computer and Information Engineering, Heze University, Heze 274015, Shandong, China, Key Laboratory of computer Information Processing, Heze University, Heze 274015, Shandong, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605