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Enhanced feature selection technique using slime mould algorithm: a case study on chemical data

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Abstract

Feature selection techniques are considered one of the most important preprocessing steps, which has the most significant influence on the performance of data analysis and decision making. These FS techniques aim to achieve several objectives (such as reducing classification error and minimizing the number of features) at the same time to increase the classification rate. FS based on Metaheuristic (MH) is considered one of the most promising techniques to improve the classification process. This paper presents a modified method of the Slime mould algorithm depending on the Marine Predators Algorithm (MPA) operators as a local search strategy, which leads to increasing the convergence rate of the developed method, named SMAMPA and avoiding the attraction to local optima. The efficiency of SMAMPA is evaluated using twenty datasets and compared its results with the state-of-the-art FS methods. In addition, the applicability of SMAMPA to work with real-world problems is evaluated by using it as a quantitative structure-activity relationship (QSAR) model. The obtained results show the high ability of the developed SMAMPA method to reduce the dimension of the tested datasets by increasing the prediction rate. In addition, it provides results better than other FS techniques in terms of performance metrics.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the UCI repository [61].

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 62150410434) and in part by LIESMARS Special Research Funding.

Author information

Authors and Affiliations

  1. Department of Information Systems, College of Computing and Information Technology, University of Bisha, Bisha, 61922, Saudi Arabia

    Ahmed A. Ewees

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

    Ahmed A. Ewees

  3. College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004, China

    Mohammed A. A. Al-qaness

  4. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  5. Faculty of Information Technology, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  6. Department of Statistics and Informatics, University of Mosul, Mosul, Iraq

    Zakariya Yahya Algamal

  7. College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq

    Zakariya Yahya Algamal

  8. Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, Mexico

    Diego Oliva

  9. Department of Electrical Engineering, Faculty of Engineering, Fayoum University, Fayoum, Egypt

    Dalia Yousri

  10. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt

    Mohamed Abd Elaziz

  11. Faculty of Computer Science and Engineering, Galala University, Suez, Egypt

    Mohamed Abd Elaziz

  12. Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, UAE

    Mohamed Abd Elaziz

  13. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon

    Mohamed Abd Elaziz

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  1. Ahmed A. Ewees
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Ewees, A.A., Al-qaness, M.A.A., Abualigah, L. et al. Enhanced feature selection technique using slime mould algorithm: a case study on chemical data. Neural Comput & Applic 35, 3307–3324 (2023). https://doi.org/10.1007/s00521-022-07852-8

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