Grey wolf optimizer with self-repulsion strategy for feature selection

Feature selection is one of the most critical steps in big data analysis. Accurately extracting correct features from massive data can effectively improve the accuracy of big data processing algorithms. However, traditional grey wolf optimizer (GWO) algorithms often suffer from slow convergence and...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 12807 - 24
Hauptverfasser:	Wang, Yufeng, Yin, Yumeng, Zhao, Hang, Liu, Jinxuan, Xu, Chunyu, Dong, Wenyong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 14.04.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/705 639/705/1042 639/705/117 Algorithms Big Data Convergence Data analysis Data processing Feature selection Grey wolf optimizer Humanities and Social Sciences Learning multidisciplinary Predatory behavior Science Science (multidisciplinary) Self-repulsion strategy Transfer function Feature selection Self-repulsion strategy Grey wolf optimizer Transfer function
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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