A vector-valued support vector machine model for multiclass problem

Ran WANG, Sam KWONG, Degang CHEN, Jingjing CAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

18 Citations (Scopus)


In this paper, a new model named Multiclass Support Vector Machines with Vector-Valued Decision (M-SVMs-VVD) or VVD is proposed. The basic idea is to separate 2a classes by a SVM hyperplanes in the feature space induced by certain kernels, where a is a finite positive integer. We start from a 2a-class problem, and extend it to any-class problem by applying a hierarchical decomposition procedure. Compared with the existing SVM-based multiclass methods, the VVD model has two advantages. First, it reduces the computational complexity by using a small number of classifiers. Second, the feature space partition induced by the hyperplanes effectively eliminates the Unclassifiable regions (URs) that may affect the classification performance of the algorithm. Experimental comparisons with several state-of-the-art multiclass methods demonstrate that VVD maintains a comparable testing accuracy, while it improves the classification efficiency with less classifiers, a smaller number of support vectors (SVs), and shorter testing time. © 2012 Elsevier Inc. All rights reserved.
Original languageEnglish
Pages (from-to)174-194
JournalInformation Sciences
Publication statusPublished - 20 Jun 2013
Externally publishedYes

Bibliographical note

This work is partially supported by City University of Hong Kong Research Grant 7002729, and the National Natural Science Foundation of China under the Grants 71171080, 61170107 and 61272289 .


  • Feature space
  • Hyperplane
  • Multiclass classification
  • Support vector machine (SVM)
  • Unclassifiable region (UR)


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