Affiliations: [a] School of Computer Science, Shaanxi Normal University, Xi'an, Shaanxi, China | [b] School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, China | [c] School of Information Systems, Computing and Mathematics, Brunel University, London, UK | [d] College of Information Engineering, Shenzhen University, Shenzhen, China | [e] CAS Research Centre on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, China
Correspondence:
[*]
Corresponding author: Juanying Xie, School of Computer Science, Shaanxi Normal University, Xi'an 710062, Shaanxi, China. E-mail: [email protected].
Abstract: Twin support vector machine classifier (TWSVM) was proposed by Jayadeva et al., which was used for binary classification problems. TWSVM not only overcomes the difficulties in handling the problem of exemplar unbalance in binary classification problems, but also it is four times faster in training a classifier than classical support vector machines. This paper proposes one-versus-all twin support vector machine classifiers (OVA-TWSVM) for multi-category classification problems by utilizing the strengths of TWSVM. OVA-TWSVM extends TWSVM to solve k-category classification problems by developing k TWSVM where in the ith TWSVM, we only solve the Quadratic Programming Problems (QPPs) for the ith class, and get the ith nonparallel hyperplane corresponding to the ith class data. OVA-TWSVM uses the well known one-versus-all (OVA) approach to construct a corresponding twin support vector machine classifier. We analyze the efficiency of the OVA-TWSVM theoretically, and perform experiments to test its efficiency on both synthetic data sets and several benchmark data sets from the UCI machine learning repository. Both the theoretical analysis and experimental results demonstrate that OVA-TWSVM can outperform the traditional OVA-SVMs classifier. Further experimental comparisons with other multiclass classifiers demonstrated that comparable performance could be achieved.
Keywords: Twin support vector machines, multicatigory data classification, multicategory twin support machine classifiers, support vector machines, pattern recognition, machine learning
