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Neural Network Based Drift Detection

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Machine Learning, Optimization, and Data Science (LOD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13810))

Abstract

The unprecedented growth in machine learning has shed light on its unique set of challenges. One such challenge is apparent changes in the input data distribution over time known as Concept Drifts. In such cases, the model’s performance degrades according to the changes in the data distribution. The remedy for concept drifts is retraining the model with the most recent data to improve the model’s performance. The significant issue is identifying the precise point at which the model must be updated for maximum performance benefits with minimum retraining effort. This problem is challenging to address in unsupervised detection methods with no access to label data to identify the changing distributions for the targets of the input data. Here, we present our unsupervised method based on a Generative Adversarial Network and a feed forward neural network for detecting concept drifts without the need for target labels. We demonstrate that our method is better at identifying concept drifts and outperforms the baseline and other comparable methods.

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Notes

  1. 1.

    https://github.com/ogozuacik/concept-drift-datasets-scikit-multiflow.

  2. 2.

    https://github.com/cfellicious/NeuralNetworkbasedDriftDetection.

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Acknowledgement

This work was partially funded by the Bundesministerium für Bildung und Forschung (BMBF, German Federal Ministry of Education and Research) – project 01IS21063A-C (SmartVMI).

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Authors and Affiliations

  1. University of Passau, 94032, Passau, Germany

    Christofer Fellicious, Lorenz Wendlinger & Michael Granitzer

Authors
  1. Christofer Fellicious
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  2. Lorenz Wendlinger
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  3. Michael Granitzer
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Corresponding author

Correspondence to Christofer Fellicious .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  6. Free University of Bozen-Bolzano, Bolzano, Italy

    Giuseppe Di Fatta

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Fellicious, C., Wendlinger, L., Granitzer, M. (2023). Neural Network Based Drift Detection. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13810. Springer, Cham. https://doi.org/10.1007/978-3-031-25599-1_28

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  • DOI: https://doi.org/10.1007/978-3-031-25599-1_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25598-4

  • Online ISBN: 978-3-031-25599-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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