A robust formulation for twin multiclass support vector machine

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.