Twin support vector machine with Universum data

Authors:
Zhiquan Qi;Yingjie Tian;Yong Shi
Affiliations:
Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, China;Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, China;Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, China and College of Information Science & Technology, University of Nebraska at Omaha, Omaha, NE ...
Venue:
Neural Networks
Year:
2012

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Abstract

The Universum, which is defined as the sample not belonging to either class of the classification problem of interest, has been proved to be helpful in supervised learning. In this work, we designed a new Twin Support Vector Machine with Universum (called U-TSVM), which can utilize Universum data to improve the classification performance of TSVM. Unlike U-SVM, in U-TSVM, Universum data are located in a nonparallel insensitive loss tube by using two Hinge Loss functions, which can exploit these prior knowledge embedded in Universum data more flexible. Empirical experiments demonstrate that U-TSVM can directly improve the classification accuracy of standard TSVM that use the labeled data alone and is superior to U-SVM in most cases.