Earticle

초록

영어

Based on fuzzy one-class support vector machine (SVM) and least squares (LS) one-class SVM, we propose an LS fuzzy one-class SVM to deal with the class imbalanced problem. The LS fuzzy one-class SVM applies a fuzzy membership to each sample and attempts to solve the modified primal problem. Hence, we just need to solve a system of linear equations as opposed solving the quadratic programming problem (QPP) in fuzzy one-class SVM, which leads to an extremely simple and fast algorithm. Numerical experiments on several benchmark data sets demonstrate the feasibility and effectiveness of the proposed algorithm.

목차

Abstract
 1. Introduction
 2. Background Review
  2.1. Fuzzy One-class Support Vector Machine
  2.2. Least Squares One-class Support Vector Machine
 3. Least Squares Fuzzy One-class Support Vector Machine
 4. Experiments
 5. Conclusion
 Acknowledgment
 References

키워드

저자

• Jingjing Zhang [ College of Science, China Agricultural University, 100083 Beijing, China ]
• Kuaini Wang [ College of Science, China Agricultural University, 100083 Beijing, China ]
• Wenxin Zhu [ Department of Basic, Science Tianjin Agricultural University, 300384 Tianjin, China ]
• Ping Zhong [ College of Science, China Agricultural University, 100083 Beijing, China ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605