In 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 - 19
Publisher
ACTA Press
Location
Salzburg, Austria
Place of publication
Anaheim, Calif.
Start date
2003-06-25
End date
2003-06-27
ISBN-13
9780889863552
ISBN-10
0889863555
Language
eng
Publication classification
E1.1 Full written paper - refereed
Copyright notice
2003, ACTA Press
Editor/Contributor(s)
M Hamza
Title of proceedings
IASTED 2003 : Proceedings of the IASTED International Conference on Intelligent Systems and Control