Model Selection in SVMs Using Differential Evolution

Jingjing Zhang, Qun Niu, Kang Li, George W. Irwin

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

To improve the performance of classification using Support Vector Machines (SVMs) while reducing the model selection time, this paper introduces Differential Evolution, a heuristic method for model selection in two-class SVMs with a RBF kernel. The model selection method and related tuning algorithm are both presented. Experimental results from application to a selection of benchmark datasets for SVMs show that this method can produce an optimized classification in less time and with higher accuracy than a classical grid search. Comparison with a Particle Swarm Optimization (PSO) based alternative is also included.
Original languageEnglish
Pages14717-14722
Number of pages6
DOIs
Publication statusPublished - 2011
Event18th IFAC World Congress - Milan, Italy
Duration: 01 Aug 201101 Aug 2011

Conference

Conference18th IFAC World Congress
CountryItaly
CityMilan
Period01/08/201101/08/2011

Bibliographical note

ISSN: 978-3-902661-93-7

Keywords

  • Fuzzy and neural systems relevant to control and identification
  • Evolutionary algorithms in control and identification

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    Zhang, J., Niu, Q., Li, K., & Irwin, G. W. (2011). Model Selection in SVMs Using Differential Evolution. 14717-14722. Paper presented at 18th IFAC World Congress, Milan, Italy. https://doi.org/10.3182/20110828-6-IT-1002.00584