Fast concept drift detection using unlabeled data

Shang, D; Zhang, G; Lu, J

Fast concept drift detection using unlabeled data

Shang, D Zhang, G

Lu, J

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Publisher:: WORLD SCIENTIFIC
Publication Type:: Conference Proceeding
Citation:: Developments of Artificial Intelligence Technologies in Computation and Robotics, 2020, pp. 133-140
Issue Date:: 2020-10

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Field	Value	Language
dc.contributor.author	Shang, D
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.date	2020-08-18
dc.date.accessioned	2021-04-23T05:11:34Z
dc.date.available	2021-04-23T05:11:34Z
dc.date.issued	2020-10
dc.identifier.citation	Developments of Artificial Intelligence Technologies in Computation and Robotics, 2020, pp. 133-140
dc.identifier.isbn	9789811223327
dc.identifier.uri	http://hdl.handle.net/10453/148313
dc.description.abstract	Streaming data mining is in use today in many industrial applications, but performance of the models is deteriorated by concept drift, especially when true labels are unavailable. This paper addresses the need of detecting concept drifts under unsupervised situation and proposes the Unsupervised Concept Drift Detection (UCDD) method. A cluster technique is first applied to determine artificial labels of the data set, then a fast drift detection algorithm is used to detect the boundary change between the labeled clusters. Through the empirical evaluation, the method demonstrates effectiveness on detecting various types of concept drifts.
dc.language	en
dc.publisher	WORLD SCIENTIFIC
dc.relation.ispartof	Developments of Artificial Intelligence Technologies in Computation and Robotics
dc.relation.ispartof	International FLINS Conference
dc.relation.isbasedon	10.1142/9789811223334_0017
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Fast concept drift detection using unlabeled data
dc.type	Conference Proceeding
utslib.location.activity	Cologne, Germany
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	*
pubs.consider-herdc	false
utslib.copyright.embargo	2021-10-01T00:00:00+1000Z
dc.date.updated	2021-04-23T05:11:33Z
pubs.finish-date	2020-08-21
pubs.place-of-publication	USA
pubs.publication-status	Published
pubs.start-date	2020-08-18
dc.location	USA

Abstract:

Streaming data mining is in use today in many industrial applications, but performance of the models is deteriorated by concept drift, especially when true labels are unavailable. This paper addresses the need of detecting concept drifts under unsupervised situation and proposes the Unsupervised Concept Drift Detection (UCDD) method. A cluster technique is first applied to determine artificial labels of the data set, then a fast drift detection algorithm is used to detect the boundary change between the labeled clusters. Through the empirical evaluation, the method demonstrates effectiveness on detecting various types of concept drifts.

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http://hdl.handle.net/10453/148313