Improved particle swarm optimization for feature selection combining simplex method and niche

Zixuan Li; Qihan Liu; Yuezhou Jing; Jiawei Xue; Shuqin Wang

doi:10.1117/12.3013611

29 November 2023 Improved particle swarm optimization for feature selection combining simplex method and niche

Zixuan Li, Qihan Liu, Yuezhou Jing, Jiawei Xue, Shuqin Wang

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129371F (2023) https://doi.org/10.1117/12.3013611
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

In order to solve the problems of local optimal solution stagnation and slow convergence speed in particle swarm optimization algorithms, this paper proposes an improved particle swarm feature selection (ISNPSO) that combines simplex method and niche. The initial solution is generated using the Relief algorithm, and the particle swarm is divided into multiple subpopulations using niche technology, allowing for information exchange between subpopulations. Local optimal particles of all subpopulations are extracted for simplex optimization, which accelerates convergence speed while maintaining diversity of the population. The performance of this method was compared with 5 algorithms on 6 datasets. The experimental results show that the algorithm effectively improves classification performance while reducing the number of features.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zixuan Li, Qihan Liu, Yuezhou Jing, Jiawei Xue, and Shuqin Wang "Improved particle swarm optimization for feature selection combining simplex method and niche", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129371F (29 November 2023); https://doi.org/10.1117/12.3013611

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