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Improved salp swarm algorithm based on particle swarm optimization for feature selection

  • Rehab Ali Ibrahim
  • Ahmed A. Ewees
  • Diego Oliva
  • Mohamed Abd Elaziz
  • Songfeng Lu
Original Research
  • 97 Downloads

Abstract

Feature selection (FS) is a machine learning process commonly used to reduce the high dimensionality problems of datasets. This task permits to extract the most representative information of high sized pools of data, reducing the computational effort in other tasks as classification. This article presents a hybrid optimization method for the FS problem; it combines the slap swarm algorithm (SSA) with the particle swarm optimization. The hybridization between both approaches creates an algorithm called SSAPSO, in which the efficacy of the exploration and the exploitation steps is improved. To verify the performance of the proposed algorithm, it is tested over two experimental series, in the first one, it is compared with other similar approaches using benchmark functions. Meanwhile, in the second set of experiments, the SSAPSO is used to determine the best set of features using different UCI datasets. Where the redundant or the confusing features are removed from the original dataset while keeping or yielding a better accuracy. The experimental results provide the evidence of the enhancement in the SSAPSO regarding the performance and the accuracy without affecting the computational effort.

Keywords

Salp swarm algorithm Particle swarm optimization Feature selection Global optimization Swarm techniques 

Notes

Acknowledgements

This work is supported by the Science and Technology Program of Shenzhen of China under Grant Nos. JCYJ20170818160208570 and JCYJ20170307160458368.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Rehab Ali Ibrahim
    • 1
  • Ahmed A. Ewees
    • 2
    • 3
  • Diego Oliva
    • 4
  • Mohamed Abd Elaziz
    • 5
  • Songfeng Lu
    • 1
    • 6
  1. 1.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.University of BishaBishaKingdom of Saudi Arabia
  3. 3.Department of ComputerDamietta UniversityDamiettaEgypt
  4. 4.Departamento de Ciencias ComputacionalesUniversidad de GuadalajaraGuadalajaraMexico
  5. 5.Department of Mathematics, Faculty of ScienceZagazig UniversityZagazigEgypt
  6. 6.Shenzhen Huazhong University of Science and Technology Research InstituteShenzhenChina

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