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Using multiple windows to track concept drift

journal contribution
posted on 2004-01-01, 00:00 authored by M Lazarescu, Svetha VenkateshSvetha Venkatesh, H Bui
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.

History

Journal

Intelligent data analysis

Volume

8

Issue

1

Pagination

29 - 59

Publisher

IOS Press

Location

Amsterdam, The Netherlands

ISSN

1088-467X

eISSN

1571-4128

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2004, IOS Press and the authors

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