Feature selection using importance-based two-stage multi-modal multiobjective particle swarm optimization

Feature selection is aimed at reducing the dimensionality of datasets while maintaining or enhancing classification accuracy. Recent studies have increasingly approached feature selection through multi-modal, multi-objective optimization. However, for high-dimensional datasets, the large decision sp...

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Bibliographic Details
Published in:	Cluster computing Vol. 28; no. 2; p. 115
Main Authors:	Ling, Qinghua, Liu, Wenkai, Han, Fei, Shi, Jinlong, Hussein, Ali Aweis, Sayway, Ben Sanvee
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.04.2025 Springer Nature B.V
Subjects:	Algorithms Classification Computer Communication Networks Computer Science Correlation coefficients Datasets Feature selection Multiple objective analysis Mutation Operating Systems Optimization algorithms Optimization techniques Pareto optimum Particle swarm optimization Processor Architectures Speciation Velocity Feature importance Feature selection Multimodal multiobjective optimization Particle swarm optimization (PSO) Niching method
ISSN:	1386-7857, 1573-7543
Online Access:	Get full text
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