Drift Adaptation via Joint Distribution Alignment

Zhang, B; Lu, J; Zhang, G

Drift Adaptation via Joint Distribution Alignment

Zhang, B Lu, J Zhang, G

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019, 2019, 00, pp. 498-504
Issue Date:: 2019-11-01

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Field	Value	Language
dc.contributor.author	Zhang, B
dc.contributor.author	Lu, J
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583
dc.date	2019-11-14
dc.date.accessioned	2021-02-02T01:17:46Z
dc.date.available	2021-02-02T01:17:46Z
dc.date.issued	2019-11-01
dc.identifier.citation	Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019, 2019, 00, pp. 498-504
dc.identifier.isbn	9781728123486
dc.identifier.uri	http://hdl.handle.net/10453/145721
dc.description.abstract	© 2019 IEEE. Machine learning in evolving environment faces challenges due to concept drift. Most concept drift adaptation methods focus on modifying the model. In this paper, a method, Drift Adaptation via Joint Distribution Alignment (DAJDA), is proposed. DAJDA performs a linear transformation to the drift instances instead of modifying model. Instances are transformed into a common feature space, reducing the discrepancy of distributions before and after drift. Experimental studies show that DAJDA has abilities to improve the performance of learning model under concept drift.
dc.language	en
dc.publisher	IEEE
dc.relation	http://purl.org/au-research/grants/arc/DP190101733
dc.relation.ispartof	Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019
dc.relation.ispartof	2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)
dc.relation.isbasedon	10.1109/ISKE47853.2019.9170335
dc.rights	© 2019 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/embargoedAccess
dc.title	Drift Adaptation via Joint Distribution Alignment
dc.type	Conference Proceeding
utslib.citation.volume	00
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2021-11-01T00:00:00+1000Z
dc.date.updated	2021-02-02T01:17:40Z
pubs.finish-date	2019-11-16
pubs.publication-status	Published
pubs.start-date	2019-11-14
pubs.volume	00

Abstract:

© 2019 IEEE. Machine learning in evolving environment faces challenges due to concept drift. Most concept drift adaptation methods focus on modifying the model. In this paper, a method, Drift Adaptation via Joint Distribution Alignment (DAJDA), is proposed. DAJDA performs a linear transformation to the drift instances instead of modifying model. Instances are transformed into a common feature space, reducing the discrepancy of distributions before and after drift. Experimental studies show that DAJDA has abilities to improve the performance of learning model under concept drift.

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http://hdl.handle.net/10453/145721