Fuzzy competence model drift detection for data-driven decision support systems

Dong, F; Zhang, G; Lu, J; Li, K

Fuzzy competence model drift detection for data-driven decision support systems

Dong, F Zhang, G

Lu, J

Li, K

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Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2018, 143 pp. 284 - 294
Issue Date:: 2018-03-01

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Field	Value	Language
dc.contributor.author	Dong, F	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Li, K	en_US
dc.date.available	2020-05-25T19:22:24Z
dc.date.issued	2018-03-01	en_US
dc.identifier.citation	Knowledge-Based Systems, 2018, 143 pp. 284 - 294	en_US
dc.identifier.issn	0950-7051	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120810
dc.description.abstract	© 2017 Elsevier B.V. This paper focuses on concept drift in business intelligence and data-driven decision support systems (DSSs). The assumption of a fixed distribution in the data renders conventional static DSSs inaccurate and unable to make correct decisions when concept drift occurs. However, it is important to know when, how, and where concept drift occurs so a DSS can adjust its decision processing knowledge to adapt to an ever-changing environment at the appropriate time. This paper presents a data distribution-based concept drift detection method called fuzzy competence model drift detection (FCM-DD). By introducing fuzzy sets theory and replacing crisp boundaries with fuzzy ones, we have improved the competence model to provide a better, more refined empirical distribution of the data stream. FCM-DD requires no prior knowledge of the underlying distribution and provides statistical guarantee of the reliability of the detected drift, based on the theory of bootstrapping. A series of experiments show that our proposed FCM-DD method can detect drift more accurately, has good sensitivity, and is robust.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP150101645
dc.relation.ispartof	Knowledge-Based Systems	en_US
dc.relation.isbasedon	10.1016/j.knosys.2017.08.018	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Fuzzy competence model drift detection for data-driven decision support systems	en_US
dc.type	Journal Article
utslib.citation.volume	143	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	1503 Business and Management	en_US
utslib.for	1702 Cognitive Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	143	en_US

Abstract:

© 2017 Elsevier B.V. This paper focuses on concept drift in business intelligence and data-driven decision support systems (DSSs). The assumption of a fixed distribution in the data renders conventional static DSSs inaccurate and unable to make correct decisions when concept drift occurs. However, it is important to know when, how, and where concept drift occurs so a DSS can adjust its decision processing knowledge to adapt to an ever-changing environment at the appropriate time. This paper presents a data distribution-based concept drift detection method called fuzzy competence model drift detection (FCM-DD). By introducing fuzzy sets theory and replacing crisp boundaries with fuzzy ones, we have improved the competence model to provide a better, more refined empirical distribution of the data stream. FCM-DD requires no prior knowledge of the underlying distribution and provides statistical guarantee of the reliability of the detected drift, based on the theory of bootstrapping. A series of experiments show that our proposed FCM-DD method can detect drift more accurately, has good sensitivity, and is robust.

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http://hdl.handle.net/10453/120810