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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Veröffentlicht in:	Future generation computer systems Jg. 155; S. 272 - 286
Hauptverfasser:	Moosavi, Syed Kumayl Raza, Saadat, Ahsan, Abaid, Zainab, Ni, Wei, Li, Kai, Guizani, Mohsen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.06.2024
Schlagworte:	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
Online-Zugang:	Volltext
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