Using multiple windows to track concept drift

Lazarescu, Mihai M., Venkatesh, Svetha and Bui, Hung H. 2004, Using multiple windows to track concept drift, Intelligent data analysis, vol. 8, no. 1, pp. 29-59.

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Title Using multiple windows to track concept drift
Author(s) Lazarescu, Mihai M.
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Bui, Hung H.
Journal name Intelligent data analysis
Volume number 8
Issue number 1
Start page 29
End page 59
Total pages 30
Publisher IOS Press
Place of publication Amsterdam, The Netherlands
Publication date 2004
ISSN 1088-467X
Summary In this paper we present a multiple window incremental learning algorithm that distinguishes between virtual concept drift and real concept drift. The algorithm is unsupervised and uses a novel approach to tracking concept drift that involves the use of competing windows to interpret the data. Unlike previous methods which use a single window to determine the drift in the data, our algorithm uses three windows of different sizes to estimate the change in the data. The advantage of this approach is that it allows the system to progressively adapt and predict the change thus enabling it to deal more effectively with different types of drift. We give a detailed description of the algorithm and present the results obtained from its application to two real world problems: background image processing and sound recognition. We also compare its performance with FLORA, an existing concept drift tracking algorithm.
Language eng
Field of Research 080104 Computer Vision
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2004, IOS Press and the authors
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