A Fuzzy Drift Correlation Matrix for Multiple Data Stream Regression

Song, Y; Zhang, G; Lu, H; Lu, J

A Fuzzy Drift Correlation Matrix for Multiple Data Stream Regression

Song, Y

Zhang, G

Lu, H

Lu, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020, 2020-July, pp. 1-6
Issue Date:: 2020-08-26

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Field	Value	Language
dc.contributor.author	Song, Y https://orcid.org/0000-0002-6633-2695
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.date	2020-07-19
dc.date.accessioned	2021-04-08T05:56:57Z
dc.date.available	2021-04-08T05:56:57Z
dc.date.issued	2020-08-26
dc.identifier.citation	2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020, 2020-July, pp. 1-6
dc.identifier.isbn	978-1-7281-6933-0
dc.identifier.issn	1544-5615
dc.identifier.uri	http://hdl.handle.net/10453/147899
dc.description.abstract	How to handle concept drift problem is a big challenge for algorithms designed for the data streams. Currently, techniques related to the concept drift problem focus on single data stream. However, it normally needs to handle multiple relevant data streams in the real-world application. Current concept drift methods can not be directly used in the multistream setting. They can only be limitedly applied on each stream separately, which omits the drift correlation between streams. In the multi-stream scenario, when drift occurs in a stream, other streams may face or have faced a similar drift problem as well. This pattern of simultaneous or delayed occurrence of drift is critical to analyze and predict multiple streams as a whole dynamic system. To fill the gap in the multi-stream scenario, this paper proposes a fuzzy drift variance (FDV) to measure the correlated drift patterns among streams. FDA is able to present how the pattern of drift occurrence for any two streams correlates and how delayed this correlation is. Seven synthetic streams are designed to validate FDA. The experimental results show a good presentation ability of FDA for drift-correlated multiple streams.
dc.language	en
dc.publisher	IEEE
dc.relation	http://purl.org/au-research/grants/arc/DP190101733
dc.relation.ispartof	2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
dc.relation.ispartof	2020 IEEE International Conference on Fuzzy Systems
dc.relation.isbasedon	10.1109/fuzz48607.2020.9177566
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A Fuzzy Drift Correlation Matrix for Multiple Data Stream Regression
dc.type	Conference Proceeding
utslib.citation.volume	2020-July
utslib.location.activity	Glasgow, United Kingdom
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2021-04-08T05:56:53Z
pubs.finish-date	2020-07-24
pubs.publication-status	Published
pubs.start-date	2020-07-19
pubs.volume	2020-July

Abstract:

How to handle concept drift problem is a big challenge for algorithms designed for the data streams. Currently, techniques related to the concept drift problem focus on single data stream. However, it normally needs to handle multiple relevant data streams in the real-world application. Current concept drift methods can not be directly used in the multistream setting. They can only be limitedly applied on each stream separately, which omits the drift correlation between streams. In the multi-stream scenario, when drift occurs in a stream, other streams may face or have faced a similar drift problem as well. This pattern of simultaneous or delayed occurrence of drift is critical to analyze and predict multiple streams as a whole dynamic system. To fill the gap in the multi-stream scenario, this paper proposes a fuzzy drift variance (FDV) to measure the correlated drift patterns among streams. FDA is able to present how the pattern of drift occurrence for any two streams correlates and how delayed this correlation is. Seven synthetic streams are designed to validate FDA. The experimental results show a good presentation ability of FDA for drift-correlated multiple streams.

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http://hdl.handle.net/10453/147899