An Improved Binary Crayfish Optimization Algorithm for Handling Feature Selection Task in Supervised Classification

An Improved Binary Crayfish Optimization Algorithm for Handling Feature Selection Task in Supervised Classification

Feature selection (FS) is a crucial phase in data mining (DM) and machine learning (ML) tasks, aimed at removing uncorrelated and redundant attributes to enhance classification accuracy. This study introduces an improved binary crayfish optimization algorithm (IBCOA) designed to tackle the FS proble...

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Bibliographic Details
Main Authors: Shaymaa E. Sorour, Lamia Hassan, Amr A. Abohany, Reda M. Hussien
Format: Article
Language:English
Published: MDPI AG 2024-07-01
Series:Mathematics
Subjects:
machine learning (ML)
feature selection (FS)
local search (LS)
crayfish optimization algorithm (CFOA)
data mining (DM)
periodic mode boundary handling
Online Access:https://www.mdpi.com/2227-7390/12/15/2364

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https://www.mdpi.com/2227-7390/12/15/2364

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