Abstract
Three block based ensembles, AWE, BWE and ACE, are considered in the perspective of learning from data streams with concept drift. AWE updates the ensemble after processing each successive block of incoming examples, while the other ensembles are additionally extended by different drift detectors. Experiments show that these extensions improve classification accuracy, in particular for sudden changes occurring within the block, as well as reduce computational costs.
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Deckert, M., Stefanowski, J. (2013). Comparing Block Ensembles for Data Streams with Concept Drift. In: Pechenizkiy, M., Wojciechowski, M. (eds) New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32518-2_7
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DOI: https://doi.org/10.1007/978-3-642-32518-2_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32517-5
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