An Improved Binary Grey-Wolf Optimizer With Simulated Annealing for Feature Selection

An Improved Binary Grey-Wolf Optimizer With Simulated Annealing for Feature Selection

This paper proposes improvements to the binary grey-wolf optimizer (BGWO) to solve the feature selection (FS) problem associated with high data dimensionality, irrelevant, noisy, and redundant data that will then allow machine learning algorithms to attain better classification/clustering accuracy i...

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Bibliographic Details
Main Authors: Mohamed Abdel-Basset, Karam M. Sallam, Reda Mohamed, Ibrahim Elgendi, Kumudu Munasinghe, Osama M. Elkomy
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Grey-wolf optimizer
feature selection
simulated annealing
mutation strategy
Online Access:https://ieeexplore.ieee.org/document/9558832/

Internet

https://ieeexplore.ieee.org/document/9558832/

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