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Support Vector Machine: Applications and Improvements Using Evolutionary Algorithms

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Evolutionary Machine Learning Techniques

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

A description of the theory and the mathematical base of support vector machines with a survey on its applications is first presented in this chapter. Then, a method for obtaining nonlinear kernel of support vector machines is proposed. The proposed method uses the gray wolf optimizer for solving the corresponding nonlinear optimization problem. A sensitivity analysis is also performed on the parameter of the model to tune the resulting classifier. The method has been applied to a set of experimental data for diabetes mellitus diagnosis. Results show that the method leads to a classifier which distinguished healthy and patient cases with 87.5% of accuracy.

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Author information

Authors and Affiliations

  1. Aerospace Research Institute, 1465774111, Tehran, Iran

    Seyed Hamed Hashemi Mehne

  2. Torrens University Australia, Brisbane, QLD, 4006, Australia

    Seyedali Mirjalili

  3. Griffith University, Brisbane, QLD, 4111, Australia

    Seyedali Mirjalili

Authors
  1. Seyed Hamed Hashemi Mehne
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  2. Seyedali Mirjalili
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Correspondence to Seyedali Mirjalili .

Editor information

Editors and Affiliations

  1. Torrens University Australia, Brisbane, QLD, Australia

    Seyedali Mirjalili

  2. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Hossam Faris

  3. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Ibrahim Aljarah

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Mehne, S.H.H., Mirjalili, S. (2020). Support Vector Machine: Applications and Improvements Using Evolutionary Algorithms. In: Mirjalili, S., Faris, H., Aljarah, I. (eds) Evolutionary Machine Learning Techniques. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-32-9990-0_3

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