Earticle

초록

영어

This paper proposes a Multiclass Least Squares Twin Support Vector Machine (MLSTSVM) classifier for multi-class classification problems. The formulation of MLSTSVM is obtained by extending the formulation of recently proposed binary Least Squares Twin Support Vector Machine (LSTSVM) classifier. For M-class classification problem, the proposed classifier seeks M-non parallel hyper-planes, one for each class, by solving M-linear equations. A regularization term is also added to improve the generalization ability. MLSTSVM works well for both linear and non-linear type of datasets. It is relatively simple and fast algorithm as compared to the other existing approaches. The performance of proposed approach has been evaluated on twelve benchmark datasets. The experimental result demonstrates the validity of proposed MLSTSVM classifier as compared to the typical multi-classifiers based on ‘Support Vector Machine’ and ‘Twin Support Vector Machine’. Statistical analysis of the proposed classifier with existing classifiers is also performed by using Friedman’s Test statistic and Nemenyi post hoc techniques.

목차

Abstract
 1. Introduction
 2. Background
  2.1 Support Vector Machine
  2.2. Twin Support Vector Machine
  2.3. Least Squares Twin Support Vector Machine
 3. Multiclass Least Squares Twin Support Vector Machine
  3.1. Linear MLSTSVM
  3.2. Non-Linear MLSTSVM
 4. Experimental Results
  4.1. Parameters Selection
  4.2. Result and Discussion
  4.3. Statistical Comparison of Classifiers
 5. Conclusion
 References

키워드

저자

• Divya Tomar [ Indian Institute of Information and Technology, Allahabad, India ]
• Sonali Agarwal [ Indian Institute of Information and Technology, Allahabad, India ]

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