A robust formulation for twin multiclass support vector machine

Julio López, Sebastián Maldonado, Miguel Carrasco

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

9 Citas (Scopus)


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.
Idioma originalInglés estadounidense
Páginas (desde-hasta)1031-1043
Número de páginas13
PublicaciónApplied Intelligence
EstadoPublicada - 1 dic. 2017

Palabras clave

  • Multiclass classification
  • Second-order cone programming
  • Support vector classification
  • Twin support vector machines


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