Comparing Block Ensembles for Data Streams with Concept Drift

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 185)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Computing SciencePoznań University of TechnologyPoznańPoland

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