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Quantum-Behaved Particle Swarm Optimization for Parameter Optimization of Support Vector Machine

Author

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  • Alaa Tharwat

    (Frankfurt University of Applied Sciences)

  • Aboul Ella Hassanien

    (Cairo University
    Scientific Research Group in Egypt (SRGE))

Abstract

Support vector machine (SVM) parameters such as penalty parameter and kernel parameters have a great influence on the complexity and accuracy of SVM model. In this paper, quantum-behaved particle swarm optimization (QPSO) has been employed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (QPSO-SVM), the experiment adopted seven standard classification datasets which are obtained from UCI machine learning data repository. For verification, the results of the QPSO-SVM algorithm are compared with the standard PSO, and genetic algorithm (GA) which is one of the well-known optimization algorithms. Moreover, the results of QPSO are compared with the grid search, which is a conventional method of searching parameter values. The experimental results demonstrated that the proposed model is capable to find the optimal values of the SVM parameters. The results also showed lower classification error rates compared with standard PSO and GA algorithms.

Suggested Citation

  • Alaa Tharwat & Aboul Ella Hassanien, 2019. "Quantum-Behaved Particle Swarm Optimization for Parameter Optimization of Support Vector Machine," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 576-598, October.
    • Handle: RePEc:spr:jclass:v:36:y:2019:i:3:d:10.1007_s00357-018-9299-1
    DOI: 10.1007/s00357-018-9299-1
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    References listed on IDEAS

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    1. Asa Ben-Hur & Cheng Soon Ong & Sören Sonnenburg & Bernhard Schölkopf & Gunnar Rätsch, 2008. "Support Vector Machines and Kernels for Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 4(10), pages 1-10, October.
    2. Gaige Wang & Lihong Guo, 2013. "A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-21, February.
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    Cited by:

    1. Qinghe Zhao & Zifang Zhang & Yuchen Huang & Junlong Fang, 2022. "TPE-RBF-SVM Model for Soybean Categories Recognition in Selected Hyperspectral Bands Based on Extreme Gradient Boosting Feature Importance Values," Agriculture, MDPI, vol. 12(9), pages 1-16, September.
    2. Qiaozhen Guo & Huanhuan Wu & Huiyi Jin & Guang Yang & Xiaoxu Wu, 2022. "Remote Sensing Inversion of Suspended Matter Concentration Using a Neural Network Model Optimized by the Partial Least Squares and Particle Swarm Optimization Algorithms," Sustainability, MDPI, vol. 14(4), pages 1-16, February.

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