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Detection & Management of Concept Drift

Mak, Lee-Onn and Krause, Paul J. (2006) Detection & Management of Concept Drift In: 2006 International Conference on Machine Learning and Cybernetics.


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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: Conference or Workshop Item (UNSPECIFIED)
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
Mak, Lee-Onn
Krause, Paul
Date : 1 August 2006
DOI : 10.1109/ICMLC.2006.258538
Uncontrolled Keywords : Bayesian Network Classifiers, Concept drift, context, context derivation
Additional Information : 2006 International Conference on Machine Learning and Cybernetics, pp. 3486 - 3491.© 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.
Depositing User : Mr Adam Field
Date Deposited : 27 May 2010 14:46
Last Modified : 06 Jul 2019 05:07

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