Journal
COGNITIVE COMPUTATION
Volume 5, Issue 4, Pages 580-588Publisher
SPRINGER
DOI: 10.1007/s12559-012-9179-7
Keywords
TSVM; Nonparallel plane; Multi-class classification; K-SVCR; Twin-KSVC
Funding
- National Natural Science Foundation of China [61153003, 11171346]
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Twin support vector machine (TSVM) is a novel machine learning algorithm, which aims at finding two nonparallel planes for each class. In order to do so, one needs to resolve a pair of smaller-sized quadratic programming problems rather than a single large one. Classical TSVM is proposed for the binary classification problem. However, multi-class classification problem is often met in our real world. For this problem, a new multi-class classification algorithm, called Twin-KSVC, is proposed in this paper. It takes the advantages of both TSVM and K-SVCR (support vector classification-regression machine for k-class classification) and evaluates all the training points into a 1-versus-1-versus-rest structure, so it generates ternary outputs {-1, 0, ? 1}. As all the samples are utilized in constructing the classification hyper-plane, our proposed algorithm yields higher classification accuracy in comparison with other two algorithms. Experimental results on eleven benchmark datasets demonstrate the feasibility and validity of our proposed algorithm.
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