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Using selective memory to track concept drift effectively
conference contribution
posted on 2003-01-01, 00:00 authored by M Lazarescu, Svetha VenkateshSvetha VenkateshIn this paper we describe a supervised learning algorithm that uses selective memory to track concept drift. Unlike previous methods to track concept drift that use window heuristics to adapt to changes, we present an improved approach that discriminates between the instances observed. The advantage of this method is that it allows the system to both adapt to and track drift more accurately as well as filter the noise in the data more effectively. We present the algorithm and compare its performance with FLORA a well known concept drift tracking algorithm.
History
Event
IASTED International Conference on Intelligent Systems and Control ( Salzburg, Austra)Pagination
14 - 19Publisher
ACTA PressLocation
Salzburg, AustriaPlace of publication
Anaheim, Calif.Start date
2003-06-25End date
2003-06-27ISBN-13
9780889863552ISBN-10
0889863555Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2003, ACTA PressEditor/Contributor(s)
M HamzaTitle of proceedings
IASTED 2003 : Proceedings of the IASTED International Conference on Intelligent Systems and ControlUsage metrics
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