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Compressed-Coding Particle Swarm Optimization for Large-Scale Feature Selection

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Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1491))

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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. 1.

    The datasets can be downloaded from https://jundongl.github.io/scikit-feature/datasets.html.

References

  1. Dash, M.: Feature selection via set cover. In: Proceedings 1997 IEEE Knowledge and Data Engineering Exchange Workshop, pp. 165–171. IEEE (1997)

    Google Scholar 

  2. Ladha, L., Deepa, T.: Feature selection methods and algorithms. Int. J. Comput. Sci. Eng. 3, 1787–1797 (2011)

    Google Scholar 

  3. Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40, 16–28 (2014)

    Article  Google Scholar 

  4. Khalid, S., Khalil, T., Nasreen, S.: A survey of feature selection and feature extraction techniques in machine learning. In: 2014 Science and Information Conference, pp. 372–378. IEEE (2014)

    Google Scholar 

  5. Nguyen, B.H., Xue, B., Zhang, M.: A survey on swarm intelligence approaches to feature selection in data mining. Swarm Evol. Comput. 54, 100663 (2020)

    Article  Google Scholar 

  6. Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20, 606–626 (2016)

    Article  Google Scholar 

  7. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 - International Conference on Neural Networks, vol.4, pp. 1942–1948. IEEE (1995)

    Google Scholar 

  8. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: Computational Cybernetics and Simulation 1997 IEEE International Conference on Systems, Man, and Cybernetics, vol.5, pp. 4104–4108. IEEE (1997)

    Google Scholar 

  9. Shen, M., Zhan, Z.H., Chen, W., Gong, Y., Zhang, J., Li, Y.: Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks. IEEE Trans. Industr. Electron. 61, 7141–7151 (2014)

    Article  Google Scholar 

  10. Qiu, C.: Bare bones particle swarm optimization with adaptive chaotic jump for feature selection in classification. Int. J. Comput. Intell. Syst. 11, 1 (2018)

    Article  Google Scholar 

  11. Gu, S., Cheng, R., Jin, Y.: Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft. Comput. 22(3), 811–822 (2016). https://doi.org/10.1007/s00500-016-2385-6

    Article  Google Scholar 

  12. Tran, B., Xue, B., Zhang, M.: Variable-length particle swarm optimization for feature selection on high-dimensional classification. IEEE Trans. Evol. Comput. 23, 473–487 (2019)

    Article  Google Scholar 

  13. Song, X., Zhang, Y., Guo, Y., Sun, X., Wang, Y.: Variable-size cooperative coevolutionary particle swarm optimization for feature selection on high-dimensional data. IEEE Trans. Evol. Comput. 24, 882–895 (2020)

    Article  Google Scholar 

  14. Bommert, A., Sun, X., Bischl, B., Rahnenführer, J., Lang, M.: Benchmark for filter methods for feature selection in high-dimensional classification data. Comput. Stat. Data Anal. 143, 106839 (2020)

    Article  MathSciNet  Google Scholar 

Download references

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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Correspondence to Zhi-Hui Zhan .

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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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  • DOI: https://doi.org/10.1007/978-981-19-4546-5_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-4545-8

  • Online ISBN: 978-981-19-4546-5

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