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Active Learning for Capturing Human Decision Policies in a Data Frugal Context

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Machine Learning, Optimization, and Data Science (LOD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13164))

Abstract

Modeling human expert decision patterns can potentially help create training and decision support systems when no ground truth data is available. A cognitive modeling approach presented herein uses a combination of supervised learning methods to mimic expert strategies. Yet without historical data logs on human expert judgments in a given domain, training machine learning algorithms with new examples to be labelled one by one by human experts can be time-consuming and costly. This paper investigates the use of active learning methods for example selection in policy capturing sessions with an oracle in order to optimize frugal learning efficiency. It also introduces a new hybrid method aimed at improving predictive accuracy based on a better management of the exploration/exploitation tradeoff. Analyses on three datasets evaluated data exploration, data exploitation and finally hybrid methods. Results highlight different tradeoffs of those methods and show the benefits of using a hybrid approach.

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Author information

Authors and Affiliations

  1. Enseirb-Matmeca Engineering School, Bordeaux, France

    Loïc Grossetête

  2. Thales Research and Technology Canada, Toronto, Canada

    Alexandre Marois, Bénédicte Chatelais & Daniel Lafond

  3. Université Laval, Quebec, Canada

    Christian Gagné

Authors
  1. Loïc Grossetête
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  2. Alexandre Marois
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  3. Bénédicte Chatelais
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  4. Christian Gagné
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  5. Daniel Lafond
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Corresponding author

Correspondence to Loïc Grossetête .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. Department of Computer Science, University of Reading, Reading, UK

    Varun Ojha

  3. Department of Computer Science, University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. Cambridge Judge Business School, University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Giorgio Jansen

  6. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Department of Informatics, Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Grossetête, L., Marois, A., Chatelais, B., Gagné, C., Lafond, D. (2022). Active Learning for Capturing Human Decision Policies in a Data Frugal Context. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science(), vol 13164. Springer, Cham. https://doi.org/10.1007/978-3-030-95470-3_30

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  • DOI: https://doi.org/10.1007/978-3-030-95470-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95469-7

  • Online ISBN: 978-3-030-95470-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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