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

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Keywords

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