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Parameter Optimization of Support Vector Machine Using Dragonfly Algorithm

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Book cover Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017 (AISI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 639))

Abstract

Support Vector Machine (SVM) parameters such as penalty and kernel parameters have a great influence on the complexity and accuracy of the classification model. In this paper, Dragonfly algorithm (DA) has been proposed to optimize the parameters of SVM; thus, the classification error can be decreased. To evaluate the proposed model (DA-SVM), the experiment adopted six standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the DA-SVM algorithm are compared with two well-known optimization algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results demonstrated that the proposed model is capable to find the optimal values of the SVM parameters and avoids the local optima problem.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Frankfurt University of Applied Sciences, Frankfurt am Main, Germany

    Alaa Tharwat & Thomas Gabel

  2. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  3. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Alaa Tharwat & Aboul Ella Hassanien

Authors
  1. Alaa Tharwat
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  2. Thomas Gabel
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  3. Aboul Ella Hassanien
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Corresponding author

Correspondence to Alaa Tharwat .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  3. Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

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Tharwat, A., Gabel, T., Hassanien, A.E. (2018). Parameter Optimization of Support Vector Machine Using Dragonfly Algorithm. In: Hassanien, A., Shaalan, K., Gaber, T., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-64861-3_29

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  • DOI: https://doi.org/10.1007/978-3-319-64861-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64860-6

  • Online ISBN: 978-3-319-64861-3

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