An Efficient Parallel Reptile Search Algorithm and Snake Optimizer Approach for Feature Selection

Ibrahim Al-Shourbaji, Pramod H. Kachare, Samah Alshathri, Salahaldeen Duraibi, Bushra Elnaim and Mohamed Abd Elaziz
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Ibrahim Al-Shourbaji: Department of Computer and Network Engineering, Jazan University, Jazan 45142, Saudi Arabia
Pramod H. Kachare: Department of Electronics & Telecomm, Engineering, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai 400706, Maharashtra, India
Samah Alshathri: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Salahaldeen Duraibi: Department of Computer and Network Engineering, Jazan University, Jazan 45142, Saudi Arabia
Bushra Elnaim: Department of Computer Science, College of Science and Humanities in Al-Sulail, Prince Sattam bin Abdulaziz University, Kharj 16278, Saudi Arabia
Mohamed Abd Elaziz: Faculty of Science & Engineering, Galala University, Suze 435611, Egypt

Mathematics, 2022, vol. 10, issue 13, 1-20

Abstract: Feature Selection (FS) is a major preprocessing stage which aims to improve Machine Learning (ML) models’ performance by choosing salient features, while reducing the computational cost. Several approaches are presented to select the most Optimal Features Subset (OFS) in a given dataset. In this paper, we introduce an FS-based approach named Reptile Search Algorithm–Snake Optimizer (RSA-SO) that employs both RSA and SO methods in a parallel mechanism to determine OFS. This mechanism decreases the chance of the two methods to stuck in local optima and it boosts the capability of both of them to balance exploration and explication. Numerous experiments are performed on ten datasets taken from the UCI repository and two real-world engineering problems to evaluate RSA-SO. The obtained results from the RSA-SO are also compared with seven popular Meta-Heuristic (MH) methods for FS to prove its superiority. The results show that the developed RSA-SO approach has a comparative performance to the tested MH methods and it can provide practical and accurate solutions for engineering optimization problems.

Keywords: classification; feature selection; metaheuristic algorithms; reptile search algorithm; snake optimizer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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