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

Ahmed A Ewees; Mohammed A A Al-Qaness; Laith Abualigah; Zakariya Yahya Algamal; Diego Oliva; Dalia Yousri; Mohamed Abd Elaziz

doi:10.1007/s00521-022-07852-8

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

Neural Comput Appl. 2023;35(4):3307-3324. doi: 10.1007/s00521-022-07852-8. Epub 2022 Oct 9.

Authors

Ahmed A Ewees^{1

2}, Mohammed A A Al-Qaness³, Laith Abualigah^{4

5}, Zakariya Yahya Algamal^{6

7}, Diego Oliva⁸, Dalia Yousri⁹, Mohamed Abd Elaziz^{10

11

12

13}

Affiliations

¹ Department of Information Systems, College of Computing and Information Technology, University of Bisha, Bisha, 61922 Saudi Arabia.
² Department of Computer, Damietta University, Damietta, 34517 Egypt.
³ College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004 China.
⁴ Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328 Jordan.
⁵ Faculty of Information Technology, Middle East University, Amman, 11831 Jordan.
⁶ Department of Statistics and Informatics, University of Mosul, Mosul, Iraq.
⁷ College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq.
⁸ Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal Mexico.
⁹ Department of Electrical Engineering, Faculty of Engineering, Fayoum University, Fayoum, Egypt.
¹⁰ Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519 Egypt.
¹¹ Faculty of Computer Science and Engineering, Galala University, Suez, Egypt.
¹² Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, UAE.
¹³ Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon.

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.

Keywords: Marine predators algorithm; Optimization feature selection; Quantitative structure-activity relationship (QSAR); Slime mould algorithm.

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