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Support Vector Machine: Applications and Improvements Using Evolutionary Algorithms

  • Seyed Hamed Hashemi Mehne
  • Seyedali MirjaliliEmail author
Chapter
Part of the Algorithms for Intelligent Systems book series (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.

Keywords

Machine learning Support vector machine Optimization Meta-heuristics Evolutionary algorithms Benchmark Artificial intelligence 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Seyed Hamed Hashemi Mehne
    • 1
  • Seyedali Mirjalili
    • 2
    • 3
    Email author
  1. 1.Aerospace Research InstituteTehranIran
  2. 2.Torrens University AustraliaBrisbaneAustralia
  3. 3.Griffith UniversityBrisbaneAustralia

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