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Machine Learning Paradigms: Theory and Application pp 351–374Cite as

Optimizing Support Vector Machine Parameters Using Bat Optimization Algorithm

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 801))

Abstract

Support Vector Machine (SVM) parameters such as penalty parameter and kernel parameters have a great influence on the complexity and accuracy of predicting model. In this research, Bat algorithm (BA) has been employed to optimize the parameters of SVM, so that the classification performance can be increased. To evaluate the proposed model (BA-SVM), the experiment adopted different standard classification datasets which are obtained from the UCI machine learning data repository. The results of the BA-SVM algorithm are compared with grid search, which is a classical method of searching parameter values, and two other optimization algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results showed that the proposed model achieved competitive results and it can find the optimal values of SVM parameters.

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Author information

Authors and Affiliations

  1. Faculty of Engineering, Suez Canal University, Ismailia, Egypt

    Alaa Tharwat

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

    Alaa Tharwat

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

    Aboul Ella Hassanien

  4. Scientific Research Group in Egypt (SRGE), Giza, Egypt

    Alaa Tharwat & Aboul Ella Hassanien

Authors
  1. Alaa Tharwat
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  2. Aboul Ella Hassanien
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Correspondence to Alaa Tharwat .

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Editors and Affiliations

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

    Aboul Ella Hassanien

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Tharwat, A., Hassanien, A.E. (2019). Optimizing Support Vector Machine Parameters Using Bat Optimization Algorithm. In: Hassanien, A. (eds) Machine Learning Paradigms: Theory and Application. Studies in Computational Intelligence, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-030-02357-7_17

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