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Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets

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Department of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia
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Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
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School of Information Technology, Skyline University College, Sharjah P.O. Box 1797, United Arab Emirates
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Author to whom correspondence should be addressed.
Academic Editor: Yudong Zhang
Received: 24 April 2021 / Revised: 17 May 2021 / Accepted: 19 May 2021 / Published: 10 June 2021
The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and reducing redundant and irrelevant features. In this study, an equilibrium optimization algorithm (EOA) is used to minimize the selected features from high-dimensional medical datasets. EOA is a novel metaheuristic physics-based algorithm and newly proposed to deal with unimodal, multi-modal, and engineering problems. EOA is considered as one of the most powerful, fast, and best performing population-based optimization algorithms. However, EOA suffers from local optima and population diversity when dealing with high dimensionality features, such as in biomedical datasets. In order to overcome these limitations and adapt EOA to solve feature selection problems, a novel metaheuristic optimizer, the so-called improved equilibrium optimization algorithm (IEOA), is proposed. Two main improvements are included in the IEOA: The first improvement is applying elite opposite-based learning (EOBL) to improve population diversity. The second improvement is integrating three novel local search strategies to prevent it from becoming stuck in local optima. The local search strategies applied to enhance local search capabilities depend on three approaches: mutation search, mutation–neighborhood search, and a backup strategy. The IEOA has enhanced the population diversity, classification accuracy, and selected features, and increased the convergence speed rate. To evaluate the performance of IEOA, we conducted experiments on 21 biomedical benchmark datasets gathered from the UCI repository. Four standard metrics were used to test and evaluate IEOA’s performance: the number of selected features, classification accuracy, fitness value, and p-value statistical test. Moreover, the proposed IEOA was compared with the original EOA and other well-known optimization algorithms. Based on the experimental results, IEOA confirmed its better performance in comparison to the original EOA and the other optimization algorithms, for the majority of the used datasets. View Full-Text
Keywords: equilibrium optimization algorithm (EOA); elite opposite-based learning (EOBL); feature selection (FS); wrapper method equilibrium optimization algorithm (EOA); elite opposite-based learning (EOBL); feature selection (FS); wrapper method
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MDPI and ACS Style

Elgamal, Z.M.; Yasin, N.M.; Sabri, A.Q.M.; Sihwail, R.; Tubishat, M.; Jarrah, H. Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets. Computation 2021, 9, 68. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9060068

AMA Style

Elgamal ZM, Yasin NM, Sabri AQM, Sihwail R, Tubishat M, Jarrah H. Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets. Computation. 2021; 9(6):68. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9060068

Chicago/Turabian Style

Elgamal, Zenab M., Norizan M. Yasin, Aznul Q.M. Sabri, Rami Sihwail, Mohammad Tubishat, and Hazim Jarrah. 2021. "Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets" Computation 9, no. 6: 68. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9060068

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