A NOVEL MULTICLASS SUPPORT VECTOR MACHINE ALGORITHM USING MEAN REVERSION AND COEFFICIENT OF VARIANCE
Bhusana Premanode, Jumlong Vongprasert, Nop Sopipan and Christofer Toumazou
DOI : 10.3844/jmssp.2013.208.218
Journal of Mathematics and Statistics
Volume 9, Issue 3
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.
© 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.