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Modified marine predators algorithm for feature selection: case study metabolomics

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Abstract

Feature selection (FS) is a necessary process applied to reduce the high dimensionality of the dataset. It is utilized to obtain the most relevant information and reduce the computational efforts of the classification process. Recently, metaheuristics methods have been widely employed for various optimization problems, including FS. In the current study, we present an FS method based on a new modified version of the marine predators algorithm (MPA). In the developed MPASCA model, the sine–cosine algorithm (SCA) is utilized to improve the search ability, which works as a local search of the MPA. To evaluate the performance of the MPASCA algorithm, extensive experiments were carried out using 18 UCI datasets. More so, the metabolomics dataset is used to test the proposed method as a real-world application. Furthermore, we implemented extensive comparisons to several state-of-art methods to verify the efficiency of the MPASCA. The evaluation outcomes showed that the MPASCA has significant performance, and it outperforms the compared methods in terms of classification measures.

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Funding

The authors received no specific funding for this work.

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt

    Mohamed Abd Elaziz

  2. Department of e-Systems, University of Bisha, Bisha, 61922, Kingdom of Saudi Arabia

    Ahmed A. Ewees

  3. Department of Computer, Damietta University, Damietta, 34517, Egypt

    Ahmed A. Ewees

  4. Engineering Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt

    Dalia Yousri

  5. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan

    Laith Abualigah

  6. School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia

    Laith Abualigah

  7. State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China

    Mohammed A. A. Al-qaness

  8. Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates

    Mohamed Abd Elaziz

  9. Department of Artificial Intelligence Science&Engineering, Galala University, Suze, 435611, Egypt

    Mohamed Abd Elaziz

  10. School of Computer Science and Robotics, Tomsk Polytechnic University, Tomsk, 634050, Russia

    Mohamed Abd Elaziz

  11. Faculty of Engineering, Sana’a University, Sana’a, 12544, Yemen

    Mohammed A. A. Al-qaness

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  1. Mohamed Abd Elaziz
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  2. Ahmed A. Ewees
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  4. Laith Abualigah
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Abd Elaziz, M., Ewees, A.A., Yousri, D. et al. Modified marine predators algorithm for feature selection: case study metabolomics. Knowl Inf Syst 64, 261–287 (2022). https://doi.org/10.1007/s10115-021-01641-w

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  • DOI: https://doi.org/10.1007/s10115-021-01641-w

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