2016

paper

english

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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10.1007/978-3-319-39315-5_1

https://link.springer.com/chapter/10.1007/978-3-319-39315-5_1

New Frontiers in Mining Complex Patterns : 4th International Workshop, NFMCP 2015, Held in Conjunction with ECML-PKDD 2015, Porto, Portugal, September 7, 2015 : Revised Selected Papers

4th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2015, 7.09.2015, Porto, Portugal

WoS (15)