Adaptive Ensembles for Evolving Data Streams – Combining Block-Based and Online Solutions
[ 1 ] Instytut Informatyki, Wydział Informatyki, Politechnika Poznańska | [ P ] pracownik
2016
referat
angielski
EN Learning ensemble classifiers from concept drifting data streams is discussed. The paper starts with a general overview of these ensembles. Then, differences between block-based and on-line ensembles are examined in detail. We hypothesize that it is still possible to develop new ensembles that combine the most beneficial properties of both types of these classifiers. Two such ensembles are described: Accuracy Updated Ensemble designed to process data blocks and its incremental version, Online Accuracy Updated Ensemble, for learning from single examples.
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