Abstract
Concept drifting is always an interesting problem. For instance, a user is interested in a set of topics, X, for a period, may switches to a different set of topics, Y, in the next period. In this paper, we focus on two issues of concept drifts, namely, concept drifts detection and model adaptation in a text stream context. We use statistical control to detect concept drifts, and propose a new multi-classifier strategy for model adaptation. We conducted extensive experiments and reported our findings in this paper.
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© 2004 Springer-Verlag Berlin Heidelberg
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Fung, G.P.C., Yu, J.X., Lu, H. (2004). Classifying Text Streams in the Presence of Concept Drifts. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_45
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DOI: https://doi.org/10.1007/978-3-540-24775-3_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22064-0
Online ISBN: 978-3-540-24775-3
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