A conditional opposition-based particle swarm optimisation for feature selection
Because of the existence of irrelevant, redundant, and noisy attributes in large datasets, the accuracy of a classification model has degraded. Hence, feature selection is a necessary pre-processing stage to select the important features that may considerably increase the efficiency of underlying cl...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2021.2002266 |
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| _version_ | 1850165796058169344 |
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| author | Jingwei Too Ali Safaa Sadiq Seyed Mohammad Mirjalili |
| author_facet | Jingwei Too Ali Safaa Sadiq Seyed Mohammad Mirjalili |
| author_sort | Jingwei Too |
| collection | DOAJ |
| description | Because of the existence of irrelevant, redundant, and noisy attributes in large datasets, the accuracy of a classification model has degraded. Hence, feature selection is a necessary pre-processing stage to select the important features that may considerably increase the efficiency of underlying classification algorithms. As a popular metaheuristic algorithm, particle swarm optimisation has successfully applied to various feature selection approaches. Nevertheless, particle swarm optimisation tends to suffer from immature convergence and low convergence rate. Besides, the imbalance between exploration and exploitation is another key issue that can significantly affect the performance of particle swarm optimisation. In this paper, a conditional opposition-based particle swarm optimisation is proposed and used to develop a wrapper feature selection. Two schemes, namely opposition-based learning and conditional strategy are introduced to enhance the performance of the particle swarm optimisation. Twenty-four benchmark datasets are used to validate the performance of the proposed approach. Furthermore, nine metaheuristics are chosen for performance verification. The findings show the supremacy of the proposed approach not only in obtaining high prediction accuracy but also in small feature sizes. |
| format | Article |
| id | doaj-art-dccd2fed9acc40fdbf35bf2b3263a816 |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-dccd2fed9acc40fdbf35bf2b3263a8162025-08-20T02:21:38ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-0134133936110.1080/09540091.2021.20022662002266A conditional opposition-based particle swarm optimisation for feature selectionJingwei Too0Ali Safaa Sadiq1Seyed Mohammad Mirjalili2Universiti Teknikal Malaysia MelakaUniversity of WolverhamptonConcordia UniversityBecause of the existence of irrelevant, redundant, and noisy attributes in large datasets, the accuracy of a classification model has degraded. Hence, feature selection is a necessary pre-processing stage to select the important features that may considerably increase the efficiency of underlying classification algorithms. As a popular metaheuristic algorithm, particle swarm optimisation has successfully applied to various feature selection approaches. Nevertheless, particle swarm optimisation tends to suffer from immature convergence and low convergence rate. Besides, the imbalance between exploration and exploitation is another key issue that can significantly affect the performance of particle swarm optimisation. In this paper, a conditional opposition-based particle swarm optimisation is proposed and used to develop a wrapper feature selection. Two schemes, namely opposition-based learning and conditional strategy are introduced to enhance the performance of the particle swarm optimisation. Twenty-four benchmark datasets are used to validate the performance of the proposed approach. Furthermore, nine metaheuristics are chosen for performance verification. The findings show the supremacy of the proposed approach not only in obtaining high prediction accuracy but also in small feature sizes.http://dx.doi.org/10.1080/09540091.2021.2002266classificationdata miningfeature selectionparticle swarm optimisationwrapper approach |
| spellingShingle | Jingwei Too Ali Safaa Sadiq Seyed Mohammad Mirjalili A conditional opposition-based particle swarm optimisation for feature selection Connection Science classification data mining feature selection particle swarm optimisation wrapper approach |
| title | A conditional opposition-based particle swarm optimisation for feature selection |
| title_full | A conditional opposition-based particle swarm optimisation for feature selection |
| title_fullStr | A conditional opposition-based particle swarm optimisation for feature selection |
| title_full_unstemmed | A conditional opposition-based particle swarm optimisation for feature selection |
| title_short | A conditional opposition-based particle swarm optimisation for feature selection |
| title_sort | conditional opposition based particle swarm optimisation for feature selection |
| topic | classification data mining feature selection particle swarm optimisation wrapper approach |
| url | http://dx.doi.org/10.1080/09540091.2021.2002266 |
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