A Binary Waterwheel Plant Optimization Algorithm for Feature Selection
The vast majority of today’s data is collected and stored in enormous databases with a wide range of characteristics that have little to do with the overarching goal concept. Feature selection is the process of choosing the best features for a classification problem, which improves the cl...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10239158/ |
| _version_ | 1797688479595888640 |
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| author | Amel Ali Alhussan Abdelaziz A. Abdelhamid El-Sayed M. El-Kenawy Abdelhameed Ibrahim Marwa Metwally Eid Doaa Sami Khafaga Ayman Em Ahmed |
| author_facet | Amel Ali Alhussan Abdelaziz A. Abdelhamid El-Sayed M. El-Kenawy Abdelhameed Ibrahim Marwa Metwally Eid Doaa Sami Khafaga Ayman Em Ahmed |
| author_sort | Amel Ali Alhussan |
| collection | DOAJ |
| description | The vast majority of today’s data is collected and stored in enormous databases with a wide range of characteristics that have little to do with the overarching goal concept. Feature selection is the process of choosing the best features for a classification problem, which improves the classification’s accuracy. Feature selection is considered a multi-objective optimization problem with two objectives: boosting classification accuracy while decreasing the feature count. To efficiently handle the feature selection process, we propose in this paper a novel algorithm inspired by the behavior of waterwheel plants when hunting their prey and how they update their locations throughout exploration and exploitation processes. The proposed algorithm is referred to as the binary waterwheel plant algorithm (bWWPA). In this particular approach, the binary search space as well as the technique’s mapping from the continuous to the discrete spaces are both represented in a new model. Specifically, the fitness and cost functions that are factored into the algorithm’s evaluation are modeled mathematically. To assess the performance of the proposed algorithm, a set of extensive experiments were conducted and evaluated in terms of 30 benchmark datasets that include low, medium, and high dimensional features. In comparison to other recent binary optimization algorithms, the experimental findings demonstrate that the bWWPA performs better than the other competing algorithms. In addition, a statistical analysis is performed in terms of the one-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests to examine the statistical differences between the proposed feature selection algorithm and compared algorithms. These experiments’ results confirmed the proposed algorithm’s superiority and effectiveness in handling the feature selection process. |
| first_indexed | 2024-03-12T01:32:33Z |
| format | Article |
| id | doaj.art-ba7884a49a14400683150ad4458cbdd8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T01:32:33Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ba7884a49a14400683150ad4458cbdd82023-09-11T23:02:09ZengIEEEIEEE Access2169-35362023-01-0111942279425110.1109/ACCESS.2023.331202210239158A Binary Waterwheel Plant Optimization Algorithm for Feature SelectionAmel Ali Alhussan0https://orcid.org/0000-0001-7530-7961Abdelaziz A. Abdelhamid1https://orcid.org/0000-0001-7080-1979El-Sayed M. El-Kenawy2https://orcid.org/0000-0002-9221-7658Abdelhameed Ibrahim3https://orcid.org/0000-0002-8352-6731Marwa Metwally Eid4Doaa Sami Khafaga5https://orcid.org/0000-0002-9843-6392Ayman Em Ahmed6https://orcid.org/0000-0002-4419-1705Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi ArabiaDepartment of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, EgyptComputer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, EgyptDepartment of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, EgyptDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaFaculty of Engineering, King Salman International University, El Tor, EgyptThe vast majority of today’s data is collected and stored in enormous databases with a wide range of characteristics that have little to do with the overarching goal concept. Feature selection is the process of choosing the best features for a classification problem, which improves the classification’s accuracy. Feature selection is considered a multi-objective optimization problem with two objectives: boosting classification accuracy while decreasing the feature count. To efficiently handle the feature selection process, we propose in this paper a novel algorithm inspired by the behavior of waterwheel plants when hunting their prey and how they update their locations throughout exploration and exploitation processes. The proposed algorithm is referred to as the binary waterwheel plant algorithm (bWWPA). In this particular approach, the binary search space as well as the technique’s mapping from the continuous to the discrete spaces are both represented in a new model. Specifically, the fitness and cost functions that are factored into the algorithm’s evaluation are modeled mathematically. To assess the performance of the proposed algorithm, a set of extensive experiments were conducted and evaluated in terms of 30 benchmark datasets that include low, medium, and high dimensional features. In comparison to other recent binary optimization algorithms, the experimental findings demonstrate that the bWWPA performs better than the other competing algorithms. In addition, a statistical analysis is performed in terms of the one-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests to examine the statistical differences between the proposed feature selection algorithm and compared algorithms. These experiments’ results confirmed the proposed algorithm’s superiority and effectiveness in handling the feature selection process.https://ieeexplore.ieee.org/document/10239158/Feature selectionwaterwheel plantmeta-heuristic optimizationK-nearest neighborsbinary optimizerbWWPA |
| spellingShingle | Amel Ali Alhussan Abdelaziz A. Abdelhamid El-Sayed M. El-Kenawy Abdelhameed Ibrahim Marwa Metwally Eid Doaa Sami Khafaga Ayman Em Ahmed A Binary Waterwheel Plant Optimization Algorithm for Feature Selection IEEE Access Feature selection waterwheel plant meta-heuristic optimization K-nearest neighbors binary optimizer bWWPA |
| title | A Binary Waterwheel Plant Optimization Algorithm for Feature Selection |
| title_full | A Binary Waterwheel Plant Optimization Algorithm for Feature Selection |
| title_fullStr | A Binary Waterwheel Plant Optimization Algorithm for Feature Selection |
| title_full_unstemmed | A Binary Waterwheel Plant Optimization Algorithm for Feature Selection |
| title_short | A Binary Waterwheel Plant Optimization Algorithm for Feature Selection |
| title_sort | binary waterwheel plant optimization algorithm for feature selection |
| topic | Feature selection waterwheel plant meta-heuristic optimization K-nearest neighbors binary optimizer bWWPA |
| url | https://ieeexplore.ieee.org/document/10239158/ |
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