Novel binary walrus optimization algorithms BWaOA and BWaOA-C with crossover operator for feature selection in high-dimensional data

Abstract Redundant and irrelevant features in high-dimensional datasets hinder the development of efficient machine learning models. Most existing Feature Selection (FS) algorithms are developed based on either embedded or filter techniques, which makes it challenging to identify the highly discrimi...

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Veröffentlicht in:	Discover Computing Jg. 28; H. 1; S. 1 - 45
Hauptverfasser:	Farid Ayeche, Adel Alti
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Springer 27.10.2025
Schlagworte:	Binary walrus optimization algorithm Classification Feature selection K-Nearest neighbor Machine learning
ISSN:	2948-2992
Online-Zugang:	Volltext
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