A robust formulation for twin multiclass support vector machine

Julio López, Sebastián Maldonado, Miguel Carrasco

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.
Original languageAmerican English
Pages (from-to)1031-1043
Number of pages13
JournalApplied Intelligence
Volume47
Issue number4
DOIs
StatePublished - 1 Dec 2017

Keywords

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

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