A hierarchical multiclass support vector machine incorporated with holistic triple learning units

X.L. Xia, Kang Li, George Irwin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs a decision tree up to a depth of A leaf node of the decision tree is allowed to be placed with a holistic triple learning unit whose generalisation abilities are assessed and approved. Meanwhile, the remaining nodes in the decision tree each accommodate a standard binary SVM classifier. The holistic triple classifier is a regression model trained on three classes, whose training algorithm is originated from a recently proposed implementation technique, namely the least-squares support vector machine (LS-SVM). A major novelty with the holistic triple classifier is the reduced number of support vectors in the solution. For the resultant HTL-SVM, an upper bound of the generalisation error can be obtained. The time complexity of training the HTL-SVM is analysed, and is shown to be comparable to that of training the one-versus-one (1-vs.-1) SVM, particularly on small-scale datasets. Empirical studies show that the proposed HTL-SVM achieves competitive classification accuracy with a reduced number of support vectors compared to the popular 1-vs-1 alternative.
Original languageEnglish
Pages (from-to)833-843
Number of pages11
JournalSoft Computing Journal
Volume15
Issue number5
DOIs
Publication statusPublished - May 2011

ASJC Scopus subject areas

  • Software
  • Geometry and Topology
  • Theoretical Computer Science

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