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      • Department of Electrical and Electronics Engineering
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      Unsupervised concept drift detection with a discriminative classifier

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      Author(s)
      Gözüaçık, Ömer
      Büyükçakır, Alican
      Bonab, H.
      Can, Fazlı
      Date
      2019
      Source Title
      Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019
      Publisher
      Association for Computing Machinery
      Pages
      2365 - 2368
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      In data stream mining, one of the biggest challenges is to develop algorithms that deal with the changing data. As data evolve over time, static models become outdated. This phenomenon is called concept drift, and it is investigated extensively in the literature. Detecting and subsequently adapting to concept drifts yield more robust and better performing models. In this study, we present an unsupervised method called D3 which uses a discriminative classifier with a sliding window to detect concept drift by monitoring changes in the feature space. It is a simple method that can be used along with any existing classifier that does not intrinsically have a drift adaptation mechanism. We experiment on the most prevalent concept drift detectors using 8 datasets. The results demonstrate that D3 outperforms the baselines, yielding models with higher performances on both real-world and synthetic datasets.
      Keywords
      Data stream
      Concept drift
      Drift detection
      Permalink
      http://hdl.handle.net/11693/52934
      Published Version (Please cite this version)
      https://dx.doi.org/10.1145/3357384.3358144
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      • Department of Computer Engineering 1510
      • Department of Electrical and Electronics Engineering 3863
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