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DOI: 10.14569/IJACSA.2016.070203
PDF

The SVM Classifier Based on the Modified Particle Swarm Optimization

Author 1: Liliya Demidova
Author 2: Evgeny Nikulchev
Author 3: Yulia Sokolova

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 2, 2016.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: The problem of development of the SVM classifier based on the modified particle swarm optimization has been considered. This algorithm carries out the simultaneous search of the kernel function type, values of the kernel function parameters and value of the regularization parameter for the SVM classifier. Such SVM classifier provides the high quality of data classification. The idea of particles' «regeneration» is put on the basis of the modified particle swarm optimization algorithm. At the realization of this idea, some particles change their kernel function type to the one which corresponds to the particle with the best value of the classification accuracy. The offered particle swarm optimization algorithm allows reducing the time expenditures for development of the SVM classifier. The results of experimental studies confirm the efficiency of this algorithm.

Keywords: particle swarm optimization; SVM-classifier; kernel function type; kernel function parameters; regularization parameter; support vectors

Liliya Demidova, Evgeny Nikulchev and Yulia Sokolova, “The SVM Classifier Based on the Modified Particle Swarm Optimization” International Journal of Advanced Computer Science and Applications(IJACSA), 7(2), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070203

@article{Demidova2016,
title = {The SVM Classifier Based on the Modified Particle Swarm Optimization},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070203},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070203},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {2},
author = {Liliya Demidova and Evgeny Nikulchev and Yulia Sokolova}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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