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Detection and Management of Concept Drift

Mak, Lee-Onn and Krause, Paul (2006) Detection and Management of Concept Drift Proceedings of the Fifth International Conference on Machine Learning and Cybernetics. pp. 3486-3491.

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Abstract

The ability to correctly detect the location and derive the contextual information where a concept begins to drift is essential in the study of domains with changing context. This paper proposes a Top-down learning method with the incorporation of a learning accuracy mechanism to efficiently detect and manage context changes within a large dataset. With the utilisation of simple search operators to perform convergent search and JBNC with a graphical viewer to derive context information, the identified hidden context are shown with the location of the disjoint points, the contextual attributes that contribute to the concept drift, the graphical output of the true relationships between these attributes and the Boolean characterisation which is the context.

Item Type: Article
Additional Information: ©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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Divisions: Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research
Depositing User: Melanie Hughes
Date Deposited: 23 Sep 2010 09:01
Last Modified: 23 Sep 2013 18:38
URI: http://epubs.surrey.ac.uk/id/eprint/2335

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