Abstract
Particle swarm optimization (PSO) is a popular method for feature selection. However, when dealing with large-scale features, PSO faces the challenges of poor search performance and long running time. In addition, a suitable representation for particles to deal with the discrete binary optimization problem like the feature selection is still in great need. This paper proposes a PSO algorithm for large-scale feature selection problems named compressed-coding PSO (CCPSO). It uses the N-base encoding method for the representation of particles and designs a particle update mechanism based on the Hamming distance, which can be performed in the discrete space. It also proposes a local search strategy to dynamically shorten the length of particles, thus reducing the search space. The experimental results show that CCPSO performs well for large-scale feature selection problems. The solutions obtained by CCPSO contain small feature subsets and have an excellent performance in classification problems.
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Notes
- 1.
The datasets can be downloaded from https://jundongl.github.io/scikit-feature/datasets.html.
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Acknowledgments
This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB2102102, in part by the National Natural Science Foundations of China (NSFC) under Grants 62176094, 61822602, 61772207, and 61873097, in part by the Key-Area Research and Development of Guangdong Province under Grant 2020B010166002, in part by the Guangdong Natural Science Foundation Research Team under Grant 2018B030312003, and in part by the Guangdong-Hong Kong Joint Innovation Platform under Grant 2018B050502006.
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Yang, JQ., Zhan, ZH., Li, T. (2022). Compressed-Coding Particle Swarm Optimization for Large-Scale Feature Selection. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1491. Springer, Singapore. https://doi.org/10.1007/978-981-19-4546-5_21
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