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Title: Adaptive concept drift detection
Authors: Dries, Anton ×
Rückert, Ulrich #
Issue Date: Dec-2009
Publisher: John Wiley & Sons, Inc.
Series Title: Statistical Analysis and Data Mining vol:2 issue:5-6 pages:311-327
Abstract: An established method to detect concept drift in data streams is to perform statistical hypothesis testing on the
multivariate data in the stream. The statistical theory offers rank-based statistics for this task. However, these statistics depend
on a fixed set of characteristics of the underlying distribution. Thus, they work well whenever the change in the underlying
distribution affects the properties measured by the statistic, but they perform not very well, if the drift influences the characteristics
caught by the test statistic only to a small degree. To address this problem, we show how uniform convergence bounds in learning
theory can be adjusted for adaptive concept drift detection. In particular, we present three novel drift detection tests, whose test
statistics are dynamically adapted to match the actual data at hand. The first one is based on a rank statistic on density estimates
for a binary representation of the data, the second compares average margins of a linear classifier induced by the 1-norm support
vector machine (SVM), and the last one is based on the average zero-one, sigmoid or stepwise linear error rate of an SVM
classifier. We compare these new approaches with the maximum mean discrepancy method, the StreamKrimp system, and the
multivariate Wald – Wolfowitz test. The results indicate that the new methods are able to detect concept drift reliably and that they
perform favorably in a precision-recall analysis.
ISSN: 1932-1864
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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