ReliefF guided variable spiral tuna swarm optimization algorithm with somersault foraging for feature selection
The feature selection (FS) technique is a powerful knowledge discovery tool for understanding complex problems by identifying the most relevant features. With the rapid development of high-throughput technologies, high-dimensional, multi-text and multi-classification data have become increasingly co...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825001346 |
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| author | Yu-Cai Wang Jie-Sheng Wang Min Zhang Hao-Ming Song Jia-Ning Hou Yu-Liang Qi Yu-Wei Song |
| author_facet | Yu-Cai Wang Jie-Sheng Wang Min Zhang Hao-Ming Song Jia-Ning Hou Yu-Liang Qi Yu-Wei Song |
| author_sort | Yu-Cai Wang |
| collection | DOAJ |
| description | The feature selection (FS) technique is a powerful knowledge discovery tool for understanding complex problems by identifying the most relevant features. With the rapid development of high-throughput technologies, high-dimensional, multi-text and multi-classification data have become increasingly common, FS is considered as an effective method for dimension reduction. So a ReliefF guided variable spiral tuna swarm optimization (TSO) algorithm with somersault foraging was proposed to solve the FS problem. Firstly, inspired by the sudden flipping behavior of manta rays when capturing plankton, a novel somersault foraging strategy is introduced to help the TSO algorithm escape from local optima. Secondly, a ReliefF-guided strategy is incorporated to add and remove features so as to improve the classification accuracy. Additionally, seven different mathematical spirals are employed to replace the original spiral foraging pattern in the TSO algorithm. By adjusting the search scope of the spiral foraging strategy, this approach enhances the search performance of the algorithm and reduces the likelihood of getting trapped in local optima. The proposed algorithm was tested on 18 UCI datasets. The first set of experiments demonstrates the effectiveness of the somersault foraging strategy, ReliefF guiding strategy and variable spiral strategy. The RReTSO CY algorithm successfully reduces the average fitness value, achieves higher classification accuracy and selects fewer features. In the second set of experiments, RReTSO CY is compared with other binary swarm intelligence optimization algorithms, with results indicating that the proposed method effectively reduces the feature subset size, improves classification accuracy and achieves the lowest average fitness value. Finally, the Wilcoxon rank-sum tests were conducted to statistically validate the effectiveness of the proposed algorithm. |
| format | Article |
| id | doaj-art-2094c9fe35c54ad78a69dcf7a8e15f4e |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-2094c9fe35c54ad78a69dcf7a8e15f4e2025-02-06T05:11:12ZengElsevierAlexandria Engineering Journal1110-01682025-04-01119168188ReliefF guided variable spiral tuna swarm optimization algorithm with somersault foraging for feature selectionYu-Cai Wang0Jie-Sheng Wang1Min Zhang2Hao-Ming Song3Jia-Ning Hou4Yu-Liang Qi5Yu-Wei Song6School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan City, Liaoning Province, PR ChinaCorresponding author.; School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan City, Liaoning Province, PR ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan City, Liaoning Province, PR ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan City, Liaoning Province, PR ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan City, Liaoning Province, PR ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan City, Liaoning Province, PR ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan City, Liaoning Province, PR ChinaThe feature selection (FS) technique is a powerful knowledge discovery tool for understanding complex problems by identifying the most relevant features. With the rapid development of high-throughput technologies, high-dimensional, multi-text and multi-classification data have become increasingly common, FS is considered as an effective method for dimension reduction. So a ReliefF guided variable spiral tuna swarm optimization (TSO) algorithm with somersault foraging was proposed to solve the FS problem. Firstly, inspired by the sudden flipping behavior of manta rays when capturing plankton, a novel somersault foraging strategy is introduced to help the TSO algorithm escape from local optima. Secondly, a ReliefF-guided strategy is incorporated to add and remove features so as to improve the classification accuracy. Additionally, seven different mathematical spirals are employed to replace the original spiral foraging pattern in the TSO algorithm. By adjusting the search scope of the spiral foraging strategy, this approach enhances the search performance of the algorithm and reduces the likelihood of getting trapped in local optima. The proposed algorithm was tested on 18 UCI datasets. The first set of experiments demonstrates the effectiveness of the somersault foraging strategy, ReliefF guiding strategy and variable spiral strategy. The RReTSO CY algorithm successfully reduces the average fitness value, achieves higher classification accuracy and selects fewer features. In the second set of experiments, RReTSO CY is compared with other binary swarm intelligence optimization algorithms, with results indicating that the proposed method effectively reduces the feature subset size, improves classification accuracy and achieves the lowest average fitness value. Finally, the Wilcoxon rank-sum tests were conducted to statistically validate the effectiveness of the proposed algorithm.http://www.sciencedirect.com/science/article/pii/S1110016825001346Feature selectionMathematical spiralReliefFSomersault foragingTuna swarm optimization algorithm |
| spellingShingle | Yu-Cai Wang Jie-Sheng Wang Min Zhang Hao-Ming Song Jia-Ning Hou Yu-Liang Qi Yu-Wei Song ReliefF guided variable spiral tuna swarm optimization algorithm with somersault foraging for feature selection Alexandria Engineering Journal Feature selection Mathematical spiral ReliefF Somersault foraging Tuna swarm optimization algorithm |
| title | ReliefF guided variable spiral tuna swarm optimization algorithm with somersault foraging for feature selection |
| title_full | ReliefF guided variable spiral tuna swarm optimization algorithm with somersault foraging for feature selection |
| title_fullStr | ReliefF guided variable spiral tuna swarm optimization algorithm with somersault foraging for feature selection |
| title_full_unstemmed | ReliefF guided variable spiral tuna swarm optimization algorithm with somersault foraging for feature selection |
| title_short | ReliefF guided variable spiral tuna swarm optimization algorithm with somersault foraging for feature selection |
| title_sort | relieff guided variable spiral tuna swarm optimization algorithm with somersault foraging for feature selection |
| topic | Feature selection Mathematical spiral ReliefF Somersault foraging Tuna swarm optimization algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825001346 |
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