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29 November 2023 Feature selection based on improved transformed length particle swarm optimization algorithm
Qihan Liu, Zixuan Li, Zhaofa Li, Yongqi Zhang, Shuqin Wang
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Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129371G (2023) https://doi.org/10.1117/12.3013446
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore
Abstract
Due to the fact that most feature selection methods based on particle swarm optimization are prone to falling into local optima, an improved transformed length particle swarm optimization algorithm (TLPSO-rr) is proposed for feature selection to address this issue. In the process of particle swarm search, mutual information is used to measure correlation and redundancy. A neighborhood search strategy is proposed to reduce redundancy, and then a transformed length strategy is used to make particles have different lengths to increase the diversity of the particle swarm and improve global search ability. To evaluate the effectiveness of this method, TLPSO-rr was compared with 7 feature selection methods on 9 different datasets. The experimental results show that this method is effective and can achieve smaller feature subsets and higher classification performance.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Qihan Liu, Zixuan Li, Zhaofa Li, Yongqi Zhang, and Shuqin Wang "Feature selection based on improved transformed length particle swarm optimization algorithm", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129371G (29 November 2023); https://doi.org/10.1117/12.3013446
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KEYWORDS
Particles
Particle swarm optimization
Feature selection
Machine learning
Data modeling
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