Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization
Feature selection is a preprocessing step for various classification tasks. Its objective is to identify the most optimal features in a dataset by eliminating redundant data while preserving the highest possible classification accuracy. Three improved binary Grey Wolf Optimization (GWO) approaches a...
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2025-01-01
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| author | Jomana Yousef Khaseeb Arabi Keshk Anas Youssef |
| author_facet | Jomana Yousef Khaseeb Arabi Keshk Anas Youssef |
| author_sort | Jomana Yousef Khaseeb |
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| description | Feature selection is a preprocessing step for various classification tasks. Its objective is to identify the most optimal features in a dataset by eliminating redundant data while preserving the highest possible classification accuracy. Three improved binary Grey Wolf Optimization (GWO) approaches are proposed in this paper to optimize the feature selection process by enhancing the feature selection accuracy while selecting the least possible number of features. Each approach combines GWO with Particle Swarm Optimization (PSO) by implementing GWO followed by PSO. Afterwards, each approach manipulates the solutions obtained by both algorithms in a different way. The objective of this combination is to overcome the GWO stuck-in-local-optima problem that might occur by leveraging the PSO-wide search space exploration ability on the solutions obtained by GWO. Both S-shaped and V-shaped binary transfer functions were used to convert the continuous solutions obtained from each proposed approach to their corresponding binary versions. The three proposed approaches were evaluated using nine small-instance, high-dimensional, cancer-related human gene expression datasets. A set of comparisons were made against the original binary versions of both GWO and PSO algorithms and against eight state-of-the-art feature selection binary optimizers in addition to one of the recent binary optimizers that combines PSO with GWO. The evaluation results showed that one of the proposed S-shaped and V-shaped approaches achieved 0.9 and 0.95 average classification accuracy, respectively, while selecting the fewest number of features. The results also confirmed the superiority of one of the proposed V-shaped approaches when compared with the original binary GWO and PSO approaches. Moreover, the results confirmed the superiority, in most of the datasets, of one of the three approaches over the state-of-the-art approaches. Finally, the results revealed that the best approach in terms of classification accuracy, fitness value, and number of selected features had the highest computational complexity. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
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| series | Applied Sciences |
| spelling | doaj-art-72b6b32ca0f048f3aafa9ee4390896e62025-01-24T13:19:33ZengMDPI AGApplied Sciences2076-34172025-01-0115248910.3390/app15020489Improved Binary Grey Wolf Optimization Approaches for Feature Selection OptimizationJomana Yousef Khaseeb0Arabi Keshk1Anas Youssef2Applied Computing Department, Palestine Technical University—Kadoorie, Ramallah P.O. Box 7, PalestineComputer Science Department, Faculty of Computers and Information, Menoufia University, Shebin El Kom 32511, EgyptComputer Science Department, Faculty of Computers and Information, Menoufia University, Shebin El Kom 32511, EgyptFeature selection is a preprocessing step for various classification tasks. Its objective is to identify the most optimal features in a dataset by eliminating redundant data while preserving the highest possible classification accuracy. Three improved binary Grey Wolf Optimization (GWO) approaches are proposed in this paper to optimize the feature selection process by enhancing the feature selection accuracy while selecting the least possible number of features. Each approach combines GWO with Particle Swarm Optimization (PSO) by implementing GWO followed by PSO. Afterwards, each approach manipulates the solutions obtained by both algorithms in a different way. The objective of this combination is to overcome the GWO stuck-in-local-optima problem that might occur by leveraging the PSO-wide search space exploration ability on the solutions obtained by GWO. Both S-shaped and V-shaped binary transfer functions were used to convert the continuous solutions obtained from each proposed approach to their corresponding binary versions. The three proposed approaches were evaluated using nine small-instance, high-dimensional, cancer-related human gene expression datasets. A set of comparisons were made against the original binary versions of both GWO and PSO algorithms and against eight state-of-the-art feature selection binary optimizers in addition to one of the recent binary optimizers that combines PSO with GWO. The evaluation results showed that one of the proposed S-shaped and V-shaped approaches achieved 0.9 and 0.95 average classification accuracy, respectively, while selecting the fewest number of features. The results also confirmed the superiority of one of the proposed V-shaped approaches when compared with the original binary GWO and PSO approaches. Moreover, the results confirmed the superiority, in most of the datasets, of one of the three approaches over the state-of-the-art approaches. Finally, the results revealed that the best approach in terms of classification accuracy, fitness value, and number of selected features had the highest computational complexity.https://www.mdpi.com/2076-3417/15/2/489swarm intelligencefeature selectionparticle swarm optimizationgrey wolf optimization |
| spellingShingle | Jomana Yousef Khaseeb Arabi Keshk Anas Youssef Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization Applied Sciences swarm intelligence feature selection particle swarm optimization grey wolf optimization |
| title | Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization |
| title_full | Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization |
| title_fullStr | Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization |
| title_full_unstemmed | Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization |
| title_short | Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization |
| title_sort | improved binary grey wolf optimization approaches for feature selection optimization |
| topic | swarm intelligence feature selection particle swarm optimization grey wolf optimization |
| url | https://www.mdpi.com/2076-3417/15/2/489 |
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