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2013 IEEE 13th International Conference on Data Mining (2013)
Dallas, TX, USA USA
Dec. 7, 2013 to Dec. 10, 2013
ISSN: 1550-4786
pp: 1319-1324
With an ever-growing availability of data streams the interest in and need for efficient techniques dealing with such data increases. A major challenge in this context is the accurate online prediction of continuous values in the presence of concept drift. In this paper, we introduce a new adaptive model tree (AMT), designed to incrementally learn from the data stream, adapt to the changes, and to perform real time accurate predictions at anytime. To deal with sub models lying in different subspaces, we propose a new model clustering algorithm able to identify subspace models, and use it for computing splits in the input space. Compared to state of the art, our AMT allows for oblique splits, delivering more compact and accurate models.
Computational modeling, Adaptation models, Data models, Predictive models, Clustering algorithms, Support vector machines, Impurities
Anca M. Zimmer, Michael Kurze, Thomas Seidl, "Adaptive Model Tree for Streaming Data", 2013 IEEE 13th International Conference on Data Mining, vol. 00, no. , pp. 1319-1324, 2013, doi:10.1109/ICDM.2013.46
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