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...
| 出版年: | Applied Soft Computing |
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| 第一著者: | |
| フォーマット: | Review |
| 言語: | English |
| 出版事項: |
Elsevier Ltd
2022
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| 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 |