Microcluster-Based Incremental Ensemble Learning for Noisy, Nonstationary Data Streams

Liu, S; Xue, S; Liu, F; Cheng, J; Li, X; Kong, C; Wu, J

Microcluster-Based Incremental Ensemble Learning for Noisy, Nonstationary Data Streams

Liu, S Xue, S Liu, F Cheng, J Li, X Kong, C Wu, J

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Publisher:: Hindawi
Publication Type:: Journal Article
Citation:: Complexity, 2020, 2020, pp. 1-12
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Liu, S
dc.contributor.author	Xue, S
dc.contributor.author	Liu, F
dc.contributor.author	Cheng, J
dc.contributor.author	Li, X
dc.contributor.author	Kong, C
dc.contributor.author	Wu, J
dc.date.accessioned	2021-03-02T04:48:47Z
dc.date.available	2021-03-02T04:48:47Z
dc.date.issued	2020-01-01
dc.identifier.citation	Complexity, 2020, 2020, pp. 1-12
dc.identifier.issn	1076-2787
dc.identifier.issn	1099-0526
dc.identifier.uri	http://hdl.handle.net/10453/146622
dc.description.abstract	© 2020 Sanmin Liu et al. Data stream classification becomes a promising prediction work with relevance to many practical environments. However, under the environment of concept drift and noise, the research of data stream classification faces lots of challenges. Hence, a new incremental ensemble model is presented for classifying nonstationary data streams with noise. Our approach integrates three strategies: incremental learning to monitor and adapt to concept drift; ensemble learning to improve model stability; and a microclustering procedure that distinguishes drift from noise and predicts the labels of incoming instances via majority vote. Experiments with two synthetic datasets designed to test for both gradual and abrupt drift show that our method provides more accurate classification in nonstationary data streams with noise than the two popular baselines.
dc.language	en
dc.publisher	Hindawi
dc.relation.ispartof	Complexity
dc.relation.isbasedon	10.1155/2020/6147378
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0102 Applied Mathematics, 0103 Numerical and Computational Mathematics, 0802 Computation Theory and Mathematics
dc.subject.classification	Fluids & Plasmas
dc.title	Microcluster-Based Incremental Ensemble Learning for Noisy, Nonstationary Data Streams
dc.type	Journal Article
utslib.citation.volume	2020
utslib.for	0102 Applied Mathematics
utslib.for	0103 Numerical and Computational Mathematics
utslib.for	0802 Computation Theory and Mathematics
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-03-02T04:48:45Z
pubs.publication-status	Published
pubs.volume	2020

Abstract:

© 2020 Sanmin Liu et al. Data stream classification becomes a promising prediction work with relevance to many practical environments. However, under the environment of concept drift and noise, the research of data stream classification faces lots of challenges. Hence, a new incremental ensemble model is presented for classifying nonstationary data streams with noise. Our approach integrates three strategies: incremental learning to monitor and adapt to concept drift; ensemble learning to improve model stability; and a microclustering procedure that distinguishes drift from noise and predicts the labels of incoming instances via majority vote. Experiments with two synthetic datasets designed to test for both gradual and abrupt drift show that our method provides more accurate classification in nonstationary data streams with noise than the two popular baselines.

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http://hdl.handle.net/10453/146622