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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Bibliographic Details

Published in:	Future generation computer systems Vol. 155; pp. 272 - 286
Main Authors:	Moosavi, Syed Kumayl Raza, Saadat, Ahsan, Abaid, Zainab, Ni, Wei, Li, Kai, Guizani, Mohsen
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.06.2024
Subjects:	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 Access:	Get full text
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