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      Unsupervised concept drift detection using a student–teacher approach

      Cerqueira, Vitor; Gomes, Heitor Murilo; Bifet, Albert
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      ECML_20_Unsupervised_Concept_Drift_Detection (1).pdf
      Accepted version, 363.1Kb
      DOI
       10.1007/s10994-022-06188-7
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      Permanent link to Research Commons version
      https://hdl.handle.net/10289/15619
      Abstract
      Concept drift detection is a crucial task in data stream evolving environments. Most of state of the art approaches designed to tackle this problem monitor the loss of predictive models. However, this approach falls short in many real-world scenarios, where the true labels are not readily available to compute the loss. In this context, there is increasing attention to approaches that perform concept drift detection in an unsupervised manner, i.e., without access to the true labels after the model is deployed. We propose a novel approach to unsupervised concept drift detection based on a student-teacher learning paradigm. Essentially, we create an auxiliary model (student) to mimic the primary model’s behaviour (teacher). At run-time, our approach is to use the teacher for predicting new instances and monitoring the mimicking loss of the student for concept drift detection. In a set of experiments using 19 data streams, we show that the proposed approach can detect concept drift and present a competitive behaviour relative to the state of the art approaches.
      Date
      2022-01-01
      Type
      Journal Article
      Rights
      This is an author’s accepted version of an article published in Machine Learning. © The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022.
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      • Science and Engineering Papers [3119]
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