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New Hybridization Algorithm of Differential Evolution and Particle Swarm Optimization for Efficient Feature Selection
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- Meng Ang Koon
- Faculty of Engineering, Technology and Built Environment, UCSI University
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- Bin Mohamed Juhari Mohd Rizon
- Faculty of Engineering, Technology and Built Environment, UCSI University
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- Hong Lim Wei
- Faculty of Engineering, Technology and Built Environment, UCSI University
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- Sun Tiang Sew
- Faculty of Engineering, Technology and Built Environment, UCSI University
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- Kit Ang Chun
- Faculty of Engineering, Technology and Built Environment, UCSI University
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- Eiyda Hussin Eryana
- Faculty of Engineering, Technology and Built Environment, UCSI University
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- Pan Li
- Faculty of Engineering, Technology and Built Environment, UCSI University
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- Hui Chong Ting
- Faculty of Engineering, Technology and Built Environment, UCSI University
Description
Feature selection is a popular pre-processing technique applied to enhance the learning performances of machine learning models by removing irrelevant features without compromising their accuracies. The rapid growth of input features in big data era has increased the complexities of feature selection problems tremendously. Given their excellent global search ability, differential evolution (DE) and particle swarm optimization (PSO) are considered as the promising techniques used to solve feature selection problems. In this paper, a new hybrid algorithm is proposed to solve feature selection problems more effectively by leveraging the strengths of both DE and PSO. The proposed feature selection algorithm is reported to achieve an average accuracy of 89.03% when solving 7 datasets obtained from UCI Machine Learning Repository.
Journal
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- Proceedings of International Conference on Artificial Life and Robotics
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Proceedings of International Conference on Artificial Life and Robotics 27 148-152, 2022-01-20
ALife Robotics Corporation Ltd.
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Keywords
Details 詳細情報について
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- CRID
- 1390854717509218304
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- ISSN
- 21887829
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- Text Lang
- en
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- Data Source
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- JaLC
- Crossref
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- Abstract License Flag
- Disallowed