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      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
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      Efficient data stream classification via probabilistic adaptive windows

      Bifet, Albert; Pfahringer, Bernhard; Read, Jesse; Holmes, Geoffrey
      DOI
       10.1145/2480362.2480516
      Link
       dl.acm.org
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      Citation
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      Bifet, A., Pfahringer, B., Read, J., & Holmes, G. (2013). Efficient data stream classification via probabilistic adaptive windows. In Proceedings of the 28th Annual ACM Symposium on Applied Computing, Coimbra, Portugal, March 18 - 22, 2013 (pp. 801-806). New York, USA: ACM.
      Permanent Research Commons link: https://hdl.handle.net/10289/7776
      Abstract
      In the context of a data stream, a classifier must be able to learn from a theoretically-infinite stream of examples using limited time and memory, while being able to predict at any point. Many methods deal with this problem by basing their model on a window of examples. We introduce a probabilistic adaptive window (PAW) for data-stream learning, which improves this windowing technique with a mechanism to include older examples as well as the most recent ones, thus maintaining information on past concept drifts while being able to adapt quickly to new ones. We exemplify PAW with lazy learning methods in two variations: one to handle concept drift explicitly, and the other to add classifier diversity using an ensemble. Along with the standard measures of accuracy and time and memory use, we compare classifiers against state-of-the-art classifiers from the data-stream literature.
      Date
      2013
      Type
      Conference Contribution
      Publisher
      ACM
      Collections
      • Computing and Mathematical Sciences Papers [1455]
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