Detecting change via competence model

Lu, N; Zhang, G; Lu, J

Detecting change via competence model

Lu, N Zhang, G

Lu, J

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Publication Type:: Chapter
Citation:: 2010, 6176 LNAI pp. 201 - 212
Issue Date:: 2010-08-02

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Field	Value	Language
dc.contributor.author	Lu, N	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.date.issued	2010-08-02	en_US
dc.identifier.citation	2010, 6176 LNAI pp. 201 - 212	en_US
dc.identifier.isbn	3642142737	en_US
dc.identifier.isbn	9783642142734	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14290
dc.description.abstract	In real world applications, interested concepts are more likely to change rather than remain stable, which is known as concept drift. This situation causes problems on predictions for many learning algorithms including case-base reasoning (CBR). When learning under concept drift, a critical issue is to identify and determine "when" and "how" the concept changes. In this paper, we developed a competence-based empirical distance between case chunks and then proposed a change detection method based on it. As a main contribution of our work, the change detection method provides an approach to measure the distribution change of cases of an infinite domain through finite samples and requires no prior knowledge about the case distribution, which makes it more practical in real world applications. Also, different from many other change detection methods, we not only detect the change of concepts but also quantify and describe this change. © 2010 Springer-Verlag.	en_US
dc.relation.isbasedon	10.1007/978-3-642-14274-1_16	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Detecting change via competence model	en_US
dc.type	Chapter
utslib.citation.volume	6176 LNAI	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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	6176 LNAI	en_US

Abstract:

In real world applications, interested concepts are more likely to change rather than remain stable, which is known as concept drift. This situation causes problems on predictions for many learning algorithms including case-base reasoning (CBR). When learning under concept drift, a critical issue is to identify and determine "when" and "how" the concept changes. In this paper, we developed a competence-based empirical distance between case chunks and then proposed a change detection method based on it. As a main contribution of our work, the change detection method provides an approach to measure the distribution change of cases of an infinite domain through finite samples and requires no prior knowledge about the case distribution, which makes it more practical in real world applications. Also, different from many other change detection methods, we not only detect the change of concepts but also quantify and describe this change. © 2010 Springer-Verlag.

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