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A robust formulation for twin multiclass support vector machine
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DOI：10.1007/s10489-017-0943-y.png)
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Abstract
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Multiclass classification is an important task in pattern analysis since numerous algorithms have been devised to predict nominal variables with multiple levels accurately. In this paper, a novel support vector machine method for twin multiclass classification is presented. The main contribution is the use of second-order cone programming as a robust setting for twin multiclass classification, in which the training patterns are represented by ellipsoids instead of reduced convex hulls. A linear formulation is derived first, while the kernel-based method is also constructed for nonlinear classification. Experiments on benchmark multiclass datasets demonstrate the virtues in terms of predictive performance of our approach.
Keywords:
Support vector classification
Multiclass classification
Twin support vector machines
Second-order cone programming
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