Abstract
The problem of recurring concepts in data stream classification is a special case of concept drift where concepts may reappear. Although several methods have been proposed that are able to learn in the presence of concept drift, few consider concept recurrence and integration of context. In this work, we extend existing drift detection methods to deal with this problem by exploiting context information associated with learned decision models in situations where concepts reappear. The preliminary experimental results demonstrate the effectiveness of the proposed approach for data stream classification problems with recurring concepts.
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Gomes, J.B., Menasalvas, E., Sousa, P.A.C. (2010). Tracking Recurrent Concepts Using Context. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds) Rough Sets and Current Trends in Computing. RSCTC 2010. Lecture Notes in Computer Science(), vol 6086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13529-3_19
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DOI: https://doi.org/10.1007/978-3-642-13529-3_19
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