Feature selection for online streaming high-dimensional data: A state-of-the-art review

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Feature selection for online streaming high-dimensional data: A state-of-the-art review

Knowledge discovery for data streaming requires online feature selection to reduce the complexity of real-world datasets and significantly improve the learning process. This is achieved by selecting highly relevant subsets and minimising irrelevant and redundant features. However, researchers have d...

書誌詳細
出版年:	Applied Soft Computing
第一著者:	2-s2.0-85135701708
フォーマット:	Review
言語:	English
出版事項:	Elsevier Ltd 2022
オンライン･アクセス:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135701708&doi=10.1016%2fj.asoc.2022.109355&partnerID=40&md5=9934c3c5266864462f837236271ec2e8

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