Induced Partitioning for Incremental Feature Selection via Rough Set Theory and Long-tail Position Grey Wolf Optimizer

Induced Partitioning for Incremental Feature Selection via Rough Set Theory and Long-tail Position Grey Wolf Optimizer

Background: Feature selection methods play a crucial role in handling challenges such as imbalanced classes, noisy data and high dimensionality. However, existing techniques, including swarm intelligence and set theory approaches, often struggle with high-dimensional datasets due to repeated reasses...

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Bibliographic Details
Main Authors: Said Al Afghani Edsa, Khamron Sunat
Format: Article
Language:English
Published: Prague University of Economics and Business 2025-01-01
Series:Acta Informatica Pragensia
Subjects:
optimizer
rough set theory
feature selection
incremental
data partitioning
Online Access:https://aip.vse.cz/artkey/aip-202501-0005_induced-partitioning-for-incremental-feature-selection-via-rough-set-theory-and-long-tail-position-grey-wolf-op.php

Internet

https://aip.vse.cz/artkey/aip-202501-0005_induced-partitioning-for-incremental-feature-selection-via-rough-set-theory-and-long-tail-position-grey-wolf-op.php

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