Adaptive threshold optimisation for online feature selection using dynamic particle swarm optimisation in determining feature relevancy and redundancy

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Adaptive threshold optimisation for online feature selection using dynamic particle swarm optimisation in determining feature relevancy and redundancy

In the era of data -driven decision -making, managing dynamic data streams characterised by evolving data distributions and high dimensionality presents a formidable challenge for online feature selection. This research addresses the challenge by developing innovative solutions in optimising Online...

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Bibliographic Details
Published in:	APPLIED SOFT COMPUTING
Main Authors:	Zaman, Ezzatul Akmal Kamaru; Ahmad, Azlin; Mohamed, Azlinah
Format:	Article
Language:	English
Published:	ELSEVIER 2024
Subjects:	Computer Science
Online Access:	https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001216508000001

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