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Improved reptile search optimization algorithm using chaotic map and simulated annealing for feature selection in medical field

The increased volume of medical datasets has produced high dimensional features, negatively affecting machine learning (ML) classifiers. In ML, the feature selection process is fundamental for selecting the most relevant features and reducing redundant and irrelevant ones. The optimization algorithm...

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Main Authors:	Elgamal, Zenab, Md Sabri, Aznul Qalid, Tubishat, Mohammad, Tbaishat, Dina, Makhadmeh, Sharif Naser, Alomari, Osama Ahmad
Format:	Article
Published:	Institute of Electrical and Electronics Engineers 2022
Subjects:	T Technology (General)
Online Access:	http://eprints.um.edu.my/42371/
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spelling	my.um.eprints.423712023-10-12T02:48:13Z http://eprints.um.edu.my/42371/ Improved reptile search optimization algorithm using chaotic map and simulated annealing for feature selection in medical field Elgamal, Zenab Md Sabri, Aznul Qalid Tubishat, Mohammad Tbaishat, Dina Makhadmeh, Sharif Naser Alomari, Osama Ahmad T Technology (General) The increased volume of medical datasets has produced high dimensional features, negatively affecting machine learning (ML) classifiers. In ML, the feature selection process is fundamental for selecting the most relevant features and reducing redundant and irrelevant ones. The optimization algorithms demonstrate its capability to solve feature selection problems. Reptile Search Algorithm (RSA) is a new nature-inspired optimization algorithm that stimulates Crocodiles' encircling and hunting behavior. The unique search of the RSA algorithm obtains promising results compared to other optimization algorithms. However, when applied to high-dimensional feature selection problems, RSA suffers from population diversity and local optima limitations. An improved metaheuristic optimizer, namely the Improved Reptile Search Algorithm (IRSA), is proposed to overcome these limitations and adapt the RSA to solve the feature selection problem. Two main improvements adding value to the standard RSA; the first improvement is to apply the chaos theory at the initialization phase of RSA to enhance its exploration capabilities in the search space. The second improvement is to combine the Simulated Annealing (SA) algorithm with the exploitation search to avoid the local optima problem. The IRSA performance was evaluated over 20 medical benchmark datasets from the UCI machine learning repository. Also, IRSA is compared with the standard RSA and state-of-the-art optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grasshopper Optimization algorithm (GOA) and Slime Mould Optimization (SMO). The evaluation metrics include the number of selected features, classification accuracy, fitness value, Wilcoxon statistical test (p-value), and convergence curve. Based on the results obtained, IRSA confirmed its superiority over the original RSA algorithm and other optimized algorithms on the majority of the medical datasets. Institute of Electrical and Electronics Engineers 2022 Article PeerReviewed Elgamal, Zenab and Md Sabri, Aznul Qalid and Tubishat, Mohammad and Tbaishat, Dina and Makhadmeh, Sharif Naser and Alomari, Osama Ahmad (2022) Improved reptile search optimization algorithm using chaotic map and simulated annealing for feature selection in medical field. IEEE Access, 10. pp. 51428-51446. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2022.3174854 <https://doi.org/10.1109/ACCESS.2022.3174854>. 10.1109/ACCESS.2022.3174854
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	T Technology (General)
spellingShingle	T Technology (General) Elgamal, Zenab Md Sabri, Aznul Qalid Tubishat, Mohammad Tbaishat, Dina Makhadmeh, Sharif Naser Alomari, Osama Ahmad Improved reptile search optimization algorithm using chaotic map and simulated annealing for feature selection in medical field
description	The increased volume of medical datasets has produced high dimensional features, negatively affecting machine learning (ML) classifiers. In ML, the feature selection process is fundamental for selecting the most relevant features and reducing redundant and irrelevant ones. The optimization algorithms demonstrate its capability to solve feature selection problems. Reptile Search Algorithm (RSA) is a new nature-inspired optimization algorithm that stimulates Crocodiles' encircling and hunting behavior. The unique search of the RSA algorithm obtains promising results compared to other optimization algorithms. However, when applied to high-dimensional feature selection problems, RSA suffers from population diversity and local optima limitations. An improved metaheuristic optimizer, namely the Improved Reptile Search Algorithm (IRSA), is proposed to overcome these limitations and adapt the RSA to solve the feature selection problem. Two main improvements adding value to the standard RSA; the first improvement is to apply the chaos theory at the initialization phase of RSA to enhance its exploration capabilities in the search space. The second improvement is to combine the Simulated Annealing (SA) algorithm with the exploitation search to avoid the local optima problem. The IRSA performance was evaluated over 20 medical benchmark datasets from the UCI machine learning repository. Also, IRSA is compared with the standard RSA and state-of-the-art optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grasshopper Optimization algorithm (GOA) and Slime Mould Optimization (SMO). The evaluation metrics include the number of selected features, classification accuracy, fitness value, Wilcoxon statistical test (p-value), and convergence curve. Based on the results obtained, IRSA confirmed its superiority over the original RSA algorithm and other optimized algorithms on the majority of the medical datasets.
format	Article
author	Elgamal, Zenab Md Sabri, Aznul Qalid Tubishat, Mohammad Tbaishat, Dina Makhadmeh, Sharif Naser Alomari, Osama Ahmad
author_facet	Elgamal, Zenab Md Sabri, Aznul Qalid Tubishat, Mohammad Tbaishat, Dina Makhadmeh, Sharif Naser Alomari, Osama Ahmad
author_sort	Elgamal, Zenab
title	Improved reptile search optimization algorithm using chaotic map and simulated annealing for feature selection in medical field
title_short	Improved reptile search optimization algorithm using chaotic map and simulated annealing for feature selection in medical field
title_full	Improved reptile search optimization algorithm using chaotic map and simulated annealing for feature selection in medical field
title_fullStr	Improved reptile search optimization algorithm using chaotic map and simulated annealing for feature selection in medical field
title_full_unstemmed	Improved reptile search optimization algorithm using chaotic map and simulated annealing for feature selection in medical field
title_sort	improved reptile search optimization algorithm using chaotic map and simulated annealing for feature selection in medical field
publisher	Institute of Electrical and Electronics Engineers
publishDate	2022
url	http://eprints.um.edu.my/42371/
_version_	1781704634515062784
score	13.211869

Improved reptile search optimization algorithm using chaotic map and simulated annealing for feature selection in medical field

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