A modified grey wolf optimizer with multi-solution crossover integration algorithm for feature selection

Feature selection helps eradicate redundant features which is essential to mitigate the curse of dimensionality when a machine-learning model deals with high-dimensional datasets. Grey Wolf Optimizer (GWO) is a swarm-based algorithm that simulates the wolves’ hunting behavior. Although very efficien...

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Vydané v:	Journal of ambient intelligence and humanized computing Ročník 16; číslo 1; s. 329 - 345
Hlavní autori:	Ihsan, Muhammad, Din, Fakhrud, Zamli, Kamal Z., Ghadi, Yazeed Yasin, Alahmadi, Tahani Jaser, Innab, Nisreen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025 Springer Nature B.V
Predmet:	Accuracy Algorithms Artificial Intelligence Classification Computational Intelligence Crossovers Datasets Engineering Exploitation Feature selection Heuristic Literature reviews Machine learning Methods Optimization Optimization algorithms Optimization techniques Original Research Pressure vessels Reforming Robotics and Automation User Interfaces and Human Computer Interaction Feature selection Swarm intelligence Grey wolf optimizer Crossover integration Engineering optimization
ISSN:	1868-5137, 1868-5145
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