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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Quick Adaptation to Changing Concepts by Sensitive Detection
verfasst von : Yoshiaki Yasumura, Naho Kitani, Kuniaki Uehara
Erschienen in: New Trends in Applied Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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In mining data streams, one of the most challenging tasks is adapting to
concept change
, that is change over time of the underlying concept in the data. In this paper, we propose a novel ensemble framework for mining concept-changing data streams. This algorithm, called QACC (
Q
uick
A
daptation to
C
hanging
C
oncepts), realizes quick adaptation to changing concepts using an ensemble of classifiers. For quick adaptation, QACC sensitively detects concept changes in noisy streaming data. Empirical studies show that the QACC algorithm is efficient for various concept changes.