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Chaotic antlion algorithm for parameter optimization of support vector machine

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Abstract

Support Vector Machine (SVM) is one of the well-known classifiers. SVM parameters such as kernel parameters and penalty parameter (C) significantly influence the classification accuracy. In this paper, a novel Chaotic Antlion Optimization (CALO) algorithm has been proposed to optimize the parameters of SVM classifier, so that the classification error can be reduced. To evaluate the proposed algorithm (CALO-SVM), the experiment adopted six standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the CALO-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, standard Ant Lion Optimization (ALO) SVM, and three well-known optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Social Emotional Optimization Algorithm (SEOA). The experimental results proved that the proposed algorithm is capable of finding the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with GA, PSO, and SEOA algorithms.

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Authors and Affiliations

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

    Alaa Tharwat

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

    Aboul Ella Hassanien

  3. Scientific Research Group in Egypt, (SRGE) http://www.egyptscience.net

    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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The authors declare that there is no conflict of interests regarding the publication of this paper.

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Tharwat, A., Hassanien, A.E. Chaotic antlion algorithm for parameter optimization of support vector machine. Appl Intell 48, 670–686 (2018). https://doi.org/10.1007/s10489-017-0994-0

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  • DOI: https://doi.org/10.1007/s10489-017-0994-0

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