Drift Detection Using Stream Volatility

  • David Tse Jung HuangEmail author
  • Yun Sing Koh
  • Gillian Dobbie
  • Albert Bifet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9284)


Current methods in data streams that detect concept drifts in the underlying distribution of data look at the distribution difference using statistical measures based on mean and variance. Existing methods are unable to proactively approximate the probability of a concept drift occurring and predict future drift points. We extend the current drift detection design by proposing the use of historical drift trends to estimate the probability of expecting a drift at different points across the stream, which we term the expected drift probability. We offer empirical evidence that applying our expected drift probability with the state-of-the-art drift detector, ADWIN, we can improve the detection performance of ADWIN by significantly reducing the false positive rate. To the best of our knowledge, this is the first work that investigates this idea. We also show that our overall concept can be easily incorporated back onto incremental classifiers such as VFDT and demonstrate that the performance of the classifier is further improved.


Data stream Drift detection Stream volatility 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • David Tse Jung Huang
    • 1
    Email author
  • Yun Sing Koh
    • 1
  • Gillian Dobbie
    • 1
  • Albert Bifet
    • 2
  1. 1.Department of Computer ScienceUniversity of AucklandAucklandNew Zealand
  2. 2.Huawei Noah’s Ark LabHong KongChina

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