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International Symposium on Intelligent Data Analysis

IDA 2015: Advances in Intelligent Data Analysis XIV pp 145–157Cite as

Probabilistic Active Learning in Datastreams

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9385)

Abstract

In recent years, stream-based active learning has become an intensively investigated research topic. In this work, we propose a new algorithm for stream-based active learning that decides immediately whether to acquire a label (selective sampling). To this purpose, we extend our pool-based Probabilistic Active Learning framework into a framework for streams. In particular, we complement the notion of usefulness within a topological space (“spatial usefulness”) with the concept of “temporal usefulness”. To actively select the instances, for which labels must be acquired, we introduce the Balanced Incremental Quantile Filter (BIQF), an algorithm that assesses the usefulness of instances in a sliding window, ensuring that the predefined budget restrictions will be met within a given tolerance window. We compare our approach to other active learning approaches for streams and show the competitiveness of our method.

Keywords

  • Window Size
  • Decision Boundary
  • Uncertainty Sampling
  • Probabilistic Gain
  • Tolerance Window

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-319-24465-5_13
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Notes

  1. 1.

    Companion website: http://kmd.cs.ovgu.de/res/pals.

  2. 2.

    More learning curves are available on http://kmd.cs.ovgu.de/res/pals.

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Author information

Authors and Affiliations

  1. Knowledge Management and Discovery Lab, Otto-von-Guericke-University, Universitätsplatz 2, 39106, Magdeburg, Germany

    Daniel Kottke, Georg Krempl & Myra Spiliopoulou

Authors
  1. Daniel Kottke
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  2. Georg Krempl
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  3. Myra Spiliopoulou
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Corresponding author

Correspondence to Daniel Kottke .

Editor information

Editors and Affiliations

  1. Université de Saint-Etienne, Saint-Etienne, France

    Elisa Fromont

  2. Intelligent Systems Lab, University of Bristol Intelligent Systems Lab, Bristol, United Kingdom

    Tijl De Bie

  3. Informatics Section, Katholieke Universiteit Leuven, Leuven, Belgium

    Matthijs van Leeuwen

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Kottke, D., Krempl, G., Spiliopoulou, M. (2015). Probabilistic Active Learning in Datastreams. In: Fromont, E., De Bie, T., van Leeuwen, M. (eds) Advances in Intelligent Data Analysis XIV. IDA 2015. Lecture Notes in Computer Science(), vol 9385. Springer, Cham. https://doi.org/10.1007/978-3-319-24465-5_13

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  • DOI: https://doi.org/10.1007/978-3-319-24465-5_13

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