Earticle

초록

영어

This research work proposes a Weighted Least Square Twin Support Vector Machine (WLSTSVM) for imbalanced dataset. Real world data are imbalanced in nature due to which most of the classification techniques do not work well. In Imbalanced data, there is a huge difference between the numbers of data samples of classes. One class data samples are larger as compared to other class data samples. This paper discusses the traditional methods of handling imbalanced data and proposes an improvement over Least Square Twin Support Vector Machine. This research work has performed experiment on five benchmark UCI datasets using 10-fold cross validation method. The results of experiment show that the proposed technique performed well for imbalanced dataset and its accuracy is better as compared to other existing methods. This research work presents the formulation of proposed approach for both linear and non-linear data samples.

목차

Abstract
 1. Introduction
 2. Least Square Twin Support Vector Machine
  2.1. For Linearly Separable Data
  2.2. For non-linear separable Data
 3. Weighted Least Square Twin Support Vector Machine
  3.1. For Linearly Separable Data
  3.2. For non-linear Separable Data
 4. Results and Experiments
 5. Conclusion
 References

키워드

저자

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