Feature selection based on dataset variance optimization using Hybrid Sine Cosine – Firehawk Algorithm (HSCFHA)

Feature selection plays a pivotal role in preprocessing data for machine learning (ML) models. It entails choosing a subset of pertinent features to enhance the model’s accuracy and minimize overfitting. Wrapper methods based on metaheuristics are one approach to feature selection, leveraging the pr...

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Vydáno v:	Future generation computer systems Ročník 155; s. 272 - 286
Hlavní autoři:	Moosavi, Syed Kumayl Raza, Saadat, Ahsan, Abaid, Zainab, Ni, Wei, Li, Kai, Guizani, Mohsen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.06.2024
Témata:	Classification Dataset variance optimization Feature selection Hybrid Sine Cosine Firehawk Algorithm Machine learning Metaheuristic algorithms Optimization algorithms Hybrid Sine Cosine Firehawk Algorithm Feature selection Machine learning Classification Optimization algorithms Dataset variance optimization Metaheuristic algorithms
ISSN:	0167-739X, 1872-7115
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