Compressed-Encoding Particle Swarm Optimization with Fuzzy Learning for Large-Scale Feature Selection
Particle swarm optimization (PSO) is a promising method for feature selection. When using PSO to solve the feature selection problem, the probability of each feature being selected and not being selected is the same in the beginning and is optimized during the evolutionary process. That is, the feat...
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MDPI AG
2022-06-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/6/1142 |
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| author | Jia-Quan Yang Chun-Hua Chen Jian-Yu Li Dong Liu Tao Li Zhi-Hui Zhan |
| author_facet | Jia-Quan Yang Chun-Hua Chen Jian-Yu Li Dong Liu Tao Li Zhi-Hui Zhan |
| author_sort | Jia-Quan Yang |
| collection | DOAJ |
| description | Particle swarm optimization (PSO) is a promising method for feature selection. When using PSO to solve the feature selection problem, the probability of each feature being selected and not being selected is the same in the beginning and is optimized during the evolutionary process. That is, the feature selection probability is optimized from symmetry (i.e., 50% vs. 50%) to asymmetry (i.e., some are selected with a higher probability, and some with a lower probability) to help particles obtain the optimal feature subset. However, when dealing with large-scale features, PSO still faces the challenges of a poor search performance and a long running time. In addition, a suitable representation for particles to deal with the discrete binary optimization problem of feature selection is still in great need. This paper proposes a compressed-encoding PSO with fuzzy learning (CEPSO-FL) for the large-scale feature selection problem. It uses the <i>N</i>-base encoding method for the representation of particles and designs a particle update mechanism based on the Hamming distance and a fuzzy learning strategy, which can be performed in the discrete space. It also proposes a local search strategy to dynamically skip some dimensions when updating particles, thus reducing the search space and reducing the running time. The experimental results show that CEPSO-FL performs well for large-scale feature selection problems. The solutions obtained by CEPSO-FL contain small feature subsets and have an excellent performance in classification problems. |
| first_indexed | 2024-03-09T22:22:02Z |
| format | Article |
| id | doaj.art-4cea5fdee64847c1aa1c449a2b062aed |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T22:22:02Z |
| publishDate | 2022-06-01 |
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| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-4cea5fdee64847c1aa1c449a2b062aed2023-11-23T19:11:30ZengMDPI AGSymmetry2073-89942022-06-01146114210.3390/sym14061142Compressed-Encoding Particle Swarm Optimization with Fuzzy Learning for Large-Scale Feature SelectionJia-Quan Yang0Chun-Hua Chen1Jian-Yu Li2Dong Liu3Tao Li4Zhi-Hui Zhan5School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, ChinaSchool of Software Engineering, South China University of Technology, Guangzhou 510006, ChinaSchool of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, ChinaSchool of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, ChinaSchool of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, ChinaSchool of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, ChinaParticle swarm optimization (PSO) is a promising method for feature selection. When using PSO to solve the feature selection problem, the probability of each feature being selected and not being selected is the same in the beginning and is optimized during the evolutionary process. That is, the feature selection probability is optimized from symmetry (i.e., 50% vs. 50%) to asymmetry (i.e., some are selected with a higher probability, and some with a lower probability) to help particles obtain the optimal feature subset. However, when dealing with large-scale features, PSO still faces the challenges of a poor search performance and a long running time. In addition, a suitable representation for particles to deal with the discrete binary optimization problem of feature selection is still in great need. This paper proposes a compressed-encoding PSO with fuzzy learning (CEPSO-FL) for the large-scale feature selection problem. It uses the <i>N</i>-base encoding method for the representation of particles and designs a particle update mechanism based on the Hamming distance and a fuzzy learning strategy, which can be performed in the discrete space. It also proposes a local search strategy to dynamically skip some dimensions when updating particles, thus reducing the search space and reducing the running time. The experimental results show that CEPSO-FL performs well for large-scale feature selection problems. The solutions obtained by CEPSO-FL contain small feature subsets and have an excellent performance in classification problems.https://www.mdpi.com/2073-8994/14/6/1142particle swarm optimizationlarge-scale feature selectionevolutionary computationfuzzy learningcompressed encodingclassification |
| spellingShingle | Jia-Quan Yang Chun-Hua Chen Jian-Yu Li Dong Liu Tao Li Zhi-Hui Zhan Compressed-Encoding Particle Swarm Optimization with Fuzzy Learning for Large-Scale Feature Selection Symmetry particle swarm optimization large-scale feature selection evolutionary computation fuzzy learning compressed encoding classification |
| title | Compressed-Encoding Particle Swarm Optimization with Fuzzy Learning for Large-Scale Feature Selection |
| title_full | Compressed-Encoding Particle Swarm Optimization with Fuzzy Learning for Large-Scale Feature Selection |
| title_fullStr | Compressed-Encoding Particle Swarm Optimization with Fuzzy Learning for Large-Scale Feature Selection |
| title_full_unstemmed | Compressed-Encoding Particle Swarm Optimization with Fuzzy Learning for Large-Scale Feature Selection |
| title_short | Compressed-Encoding Particle Swarm Optimization with Fuzzy Learning for Large-Scale Feature Selection |
| title_sort | compressed encoding particle swarm optimization with fuzzy learning for large scale feature selection |
| topic | particle swarm optimization large-scale feature selection evolutionary computation fuzzy learning compressed encoding classification |
| url | https://www.mdpi.com/2073-8994/14/6/1142 |
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