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...

Celý popis

Podrobná bibliografia

Vydané v:	Future generation computer systems Ročník 155; s. 272 - 286
Hlavní autori:	Moosavi, Syed Kumayl Raza, Saadat, Ahsan, Abaid, Zainab, Ni, Wei, Li, Kai, Guizani, Mohsen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.06.2024
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!