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International Conference on Discovery Science

DS 2023: Discovery Science pp 265–276Cite as

Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 14276)

Abstract

Active learning aims to reduce the amount of labeled data while maximizing machine learning models’ performances. Currently, there is sparse research on the potential of an optimal active learning strategy. Therefore, we propose a non-myopic oracle policy that accesses the true labels of the data pool to approximate an optimal active learning strategy. We evaluate how the hyperparameters of this oracle policy influence its performance and empirically demonstrate that it is an upper baseline for common active learning strategies while being faster than a state-of-the-art oracle policy. For the sake of reproducibility, all the code related to our research is publicly available on our GitHub repository at https://github.com/ies-research/non-myopic-oracle-policy.

Keywords

  • Active Learning
  • Oracle Policy
  • Classification

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Author information

Authors and Affiliations

  1. University of Kassel, Wilhelmshöher Allee 73, 34121, Kassel, Germany

    Christoph Sandrock, Marek Herde, Daniel Kottke & Bernhard Sick

Authors
  1. Christoph Sandrock
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  2. Marek Herde
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  3. Daniel Kottke
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  4. Bernhard Sick
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Corresponding author

Correspondence to Christoph Sandrock .

Editor information

Editors and Affiliations

  1. Waikato University, Hamilton, New Zealand

    Albert Bifet

  2. Aeronautics Institute of Technology, São José dos Campos, Brazil

    Ana Carolina Lorena

  3. University of Porto, Porto, Portugal

    Rita P. Ribeiro

  4. University of Porto, Porto, Portugal

    João Gama

  5. University of Coimbra, Coimbra, Portugal

    Pedro H. Abreu

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Sandrock, C., Herde, M., Kottke, D., Sick, B. (2023). Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy. In: Bifet, A., Lorena, A.C., Ribeiro, R.P., Gama, J., Abreu, P.H. (eds) Discovery Science. DS 2023. Lecture Notes in Computer Science(), vol 14276. Springer, Cham. https://doi.org/10.1007/978-3-031-45275-8_18

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  • DOI: https://doi.org/10.1007/978-3-031-45275-8_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45274-1

  • Online ISBN: 978-3-031-45275-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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