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ISSN: 1549-3644 (Print)
ISSN: 1558-6359 (Online)
Research Article Open Access

A NOVEL MULTICLASS SUPPORT VECTOR MACHINE ALGORITHM USING MEAN REVERSION AND COEFFICIENT OF VARIANCE

Bhusana Premanode1, Jumlong Vongprasert2, Nop Sopipan3 and Christofer Toumazou1
  • 1 , United Kingdom
  • 2 Ubon Rachathani Rajabhat University, Thailand
  • 3 Nakhon Ratchasima Rajabhat University, Thailand

Abstract

Inaccuracy of a kernel function used in Support Vector Machine (SVM) can be found when simulated with nonlinear and stationary datasets. To minimise the error, we propose a new multiclass SVM model using mean reversion and coefficient of variance algorithm to partition and classify imbalance in datasets. By introducing a series of test statistic, simulations of the proposed algorithm outperformed the performance of the SVM model without using multiclass SVM model.

Journal of Mathematics and Statistics
Volume 9 No. 3, 2013, 208-218

DOI: https://doi.org/10.3844/jmssp.2013.208.218

Submitted On: 31 March 2013 Published On: 31 July 2013

How to Cite: Premanode, B., Vongprasert, J., Sopipan, N. & Toumazou, C. (2013). A NOVEL MULTICLASS SUPPORT VECTOR MACHINE ALGORITHM USING MEAN REVERSION AND COEFFICIENT OF VARIANCE. Journal of Mathematics and Statistics, 9(3), 208-218. https://doi.org/10.3844/jmssp.2013.208.218

Open AccessCopyright: © 2013 Bhusana Premanode, Jumlong Vongprasert, Nop Sopipan and Christofer Toumazou. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Keywords

  • Support Vector Machine
  • Multiclass
  • Mean Reversion
  • Coefficient of Variance