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The SVM Classifier Based on the Modified Particle Swarm Optimization

International journal of advanced computer science & applications, 2016-01, Vol.7 (2)

2016. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2158-107X ;EISSN: 2156-5570 ;DOI: 10.14569/IJACSA.2016.070203

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  • Title:
    The SVM Classifier Based on the Modified Particle Swarm Optimization
  • Author: Demidova, Liliya ; Nikulchev, Evgeny ; Sokolova, Yulia
  • Subjects: Algorithms ; Classification ; Classifiers ; Expenditures ; Kernel functions ; Optimization algorithms ; Parameters ; Particle swarm optimization ; Regularization ; Support vector machines
  • Is Part Of: International journal of advanced computer science & applications, 2016-01, Vol.7 (2)
  • Description: 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.
  • Publisher: West Yorkshire: Science and Information (SAI) Organization Limited
  • Language: English
  • Identifier: ISSN: 2158-107X
    EISSN: 2156-5570
    DOI: 10.14569/IJACSA.2016.070203
  • Source: ProQuest Central
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