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A drift detection method based on dynamic classifier selection

  • Felipe PinagéEmail author
  • Eulanda M. dos Santos
  • João Gama
Article
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Abstract

Machine learning algorithms can be applied to several practical problems, such as spam, fraud and intrusion detection, and customer preferences, among others. In most of these problems, data come in streams, which mean that data distribution may change over time, leading to concept drift. The literature is abundant on providing supervised methods based on error monitoring for explicit drift detection. However, these methods may become infeasible in some real-world applications—where there is no fully labeled data available, and may depend on a significant decrease in accuracy to be able to detect drifts. There are also methods based on blind approaches, where the decision model is updated constantly. However, this may lead to unnecessary system updates. In order to overcome these drawbacks, we propose in this paper a semi-supervised drift detector that uses an ensemble of classifiers based on self-training online learning and dynamic classifier selection. For each unknown sample, a dynamic selection strategy is used to choose among the ensemble’s component members, the classifier most likely to be the correct one for classifying it. The prediction assigned by the chosen classifier is used to compute an estimate of the error produced by the ensemble members. The proposed method monitors such a pseudo-error in order to detect drifts and to update the decision model only after drift detection. The achievement of this method is relevant in that it allows drift detection and reaction and is applicable in several practical problems. The experiments conducted indicate that the proposed method attains high performance and detection rates, while reducing the amount of labeled data used to detect drift.

Keywords

Concept drift Drift detection Ensemble classifiers Self-training Data streams 

Notes

Acknowledgements

The authors gratefully acknowledge the financial support granted by PNPD-CAPES (Coordination for the Improvement of Higher Education Personnel) and FAPEAM (Amazonas Research Foundation) for this research through process Number 009/2012 (RHTI-Doutorado).

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Copyright information

© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of ComputingFederal University of AmazonasManausBrazil
  2. 2.Department of InformaticsFederal University of ParanáCuritibaBrazil
  3. 3.Institute of Engineering and Computer SystemsUniversity of PortoPortoPortugal

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