An Opposition-Based Great Wall Construction Metaheuristic Algorithm With Gaussian Mutation for Feature Selection
The feature selection problem involves selecting a subset of relevant features to enhance the performance of machine learning models, crucial for achieving model accuracy. Its complexity arises from the vast search space, necessitating the application of metaheuristic methods to efficiently identify...
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2024-01-01
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| author | Farouq Zitouni Abdulaziz S. Almazyad Guojiang Xiong Ali Wagdy Mohamed Saad Harous |
| author_facet | Farouq Zitouni Abdulaziz S. Almazyad Guojiang Xiong Ali Wagdy Mohamed Saad Harous |
| author_sort | Farouq Zitouni |
| collection | DOAJ |
| description | The feature selection problem involves selecting a subset of relevant features to enhance the performance of machine learning models, crucial for achieving model accuracy. Its complexity arises from the vast search space, necessitating the application of metaheuristic methods to efficiently identify optimal feature subsets. In this work, we employed a recently proposed metaheuristic algorithm named the Great Wall Construction Algorithm to address this challenge – a powerful optimizer with promising results. To enhance the algorithm’s performance in terms of exploration, exploitation, and avoidance of local optima, we integrated opposition-based learning and Gaussian mutation techniques. The proposed algorithm underwent a comprehensive comparative analysis against ten influential state-of-the-art methodologies, encompassing seven contemporary algorithms and three classical counterparts. The evaluation covered 22 datasets of varying sizes, ranging from 9 to 856 features, and included the utilization of six distinct evaluation metrics related to accuracy, classification error rate, number of selected features, and completion time to facilitate comprehensive comparisons. The obtained numerical results underwent rigorous scrutiny through several non-parametric statistical tests, including the Friedman test, the post hoc Dunn’s test, and the Wilcoxon signed ranks test. The resulting mean ranks and p-values unequivocally demonstrate the superior efficacy of the proposed algorithm in addressing the feature selection problem. The Matlab source code for the proposed approach is available for access via the link “<uri>https://www.mathworks.com/matlabcentral/fileexchange/159728-an-opposition-based-gwca-for-thefs-problem</uri>”. |
| format | Article |
| id | doaj-art-cb087be5e28d4251b6c4247b22f4bb09 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-cb087be5e28d4251b6c4247b22f4bb092025-08-20T02:53:09ZengIEEEIEEE Access2169-35362024-01-0112307963082310.1109/ACCESS.2024.336744010440097An Opposition-Based Great Wall Construction Metaheuristic Algorithm With Gaussian Mutation for Feature SelectionFarouq Zitouni0Abdulaziz S. Almazyad1Guojiang Xiong2Ali Wagdy Mohamed3https://orcid.org/0000-0002-5895-2632Saad Harous4https://orcid.org/0000-0001-6524-7352Department of Computer Science and Information Technology, Kasdi Merbah University, Ouargla, AlgeriaDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaGuizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang, ChinaOperations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, EgyptDepartment of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab EmiratesThe feature selection problem involves selecting a subset of relevant features to enhance the performance of machine learning models, crucial for achieving model accuracy. Its complexity arises from the vast search space, necessitating the application of metaheuristic methods to efficiently identify optimal feature subsets. In this work, we employed a recently proposed metaheuristic algorithm named the Great Wall Construction Algorithm to address this challenge – a powerful optimizer with promising results. To enhance the algorithm’s performance in terms of exploration, exploitation, and avoidance of local optima, we integrated opposition-based learning and Gaussian mutation techniques. The proposed algorithm underwent a comprehensive comparative analysis against ten influential state-of-the-art methodologies, encompassing seven contemporary algorithms and three classical counterparts. The evaluation covered 22 datasets of varying sizes, ranging from 9 to 856 features, and included the utilization of six distinct evaluation metrics related to accuracy, classification error rate, number of selected features, and completion time to facilitate comprehensive comparisons. The obtained numerical results underwent rigorous scrutiny through several non-parametric statistical tests, including the Friedman test, the post hoc Dunn’s test, and the Wilcoxon signed ranks test. The resulting mean ranks and p-values unequivocally demonstrate the superior efficacy of the proposed algorithm in addressing the feature selection problem. The Matlab source code for the proposed approach is available for access via the link “<uri>https://www.mathworks.com/matlabcentral/fileexchange/159728-an-opposition-based-gwca-for-thefs-problem</uri>”.https://ieeexplore.ieee.org/document/10440097/Feature selection problemgreat wall construction metaheuristic algorithmopposition-based learningGaussian mutation |
| spellingShingle | Farouq Zitouni Abdulaziz S. Almazyad Guojiang Xiong Ali Wagdy Mohamed Saad Harous An Opposition-Based Great Wall Construction Metaheuristic Algorithm With Gaussian Mutation for Feature Selection IEEE Access Feature selection problem great wall construction metaheuristic algorithm opposition-based learning Gaussian mutation |
| title | An Opposition-Based Great Wall Construction Metaheuristic Algorithm With Gaussian Mutation for Feature Selection |
| title_full | An Opposition-Based Great Wall Construction Metaheuristic Algorithm With Gaussian Mutation for Feature Selection |
| title_fullStr | An Opposition-Based Great Wall Construction Metaheuristic Algorithm With Gaussian Mutation for Feature Selection |
| title_full_unstemmed | An Opposition-Based Great Wall Construction Metaheuristic Algorithm With Gaussian Mutation for Feature Selection |
| title_short | An Opposition-Based Great Wall Construction Metaheuristic Algorithm With Gaussian Mutation for Feature Selection |
| title_sort | opposition based great wall construction metaheuristic algorithm with gaussian mutation for feature selection |
| topic | Feature selection problem great wall construction metaheuristic algorithm opposition-based learning Gaussian mutation |
| url | https://ieeexplore.ieee.org/document/10440097/ |
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