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Summary: Partial Drift Detection Using a Rule Induction Framework
Damon Sotoudeh
York University, Toronto, Canada
4700 Keele Street
Toronto, Canada
damon@cse.yorku.ca
Aijun An
York University, Toronto, Canada
4700 Keele Street
Toronto, Canada
aan@cse.yorku.ca
ABSTRACT
The major challenge in mining data streams is the issue of
concept drift, the tendency of the underlying data genera-
tion process to change over time. In this paper, we propose
a general rule learning framework that can efficiently handle
concept-drifting data streams and maintain a highly accu-
rate classification model. The main idea is to focus on partial
drifts by allowing individual rules to monitor the stream and
detect if there is a drift in the regions they cover. A rule
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