Combining a global SVM and local nearest-neighbor classifiers driven by local discriminative boundaries

Wei Xiong, S. H. Ong, T. T. Le, Joo Hwee Lim, Jiang Liu, Kelvin Foong

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Nonlinear support vector machines (SVMs) rely on the kernel trick and tradeoff parameters to build nonlinear models to classify complex problems and balance misclassification and generalization. The inconvenience in determining the kernel and the parameters has motivated the use of local nearest neighbor (NN) classifiers in lieu of global classifiers. This substitution ignores the advantage of SVM in global error minimization. On the other hand, the NN rule assumes that class conditional probabilities are locally constant. Such an assumption does not hold near class boundaries and in any high dimensional space due to the curse of dimensionality. We propose a hybrid classification method combining the global SVM and local NN classifiers. Local classifiers occur only when the global SVM is likely to fail. Furthermore, local NN classifiers adopt an adaptive metric driven by local SVM discriminative boundaries. Improved performance has been demonstrated compared to partially similar.

Original languageEnglish
Title of host publication2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009
Pages3597-3600
Number of pages4
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009 - Xi'an, China
Duration: 25 May 200927 May 2009

Publication series

Name2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009

Conference

Conference2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009
Country/TerritoryChina
CityXi'an
Period25/05/0927/05/09

Keywords

  • Adaptive metric
  • Boundary driven
  • Combination
  • Local
  • Nearest neighbors
  • Support vector machines

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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