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ImbalancedLearningRegression - A Python Package to Tackle the Imbalanced Regression Problem

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Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13718))

Abstract

This package helps Python users address imbalanced regression problems. Popular Python packages exist for imbalanced classification. However, there is still little Python support for imbalanced regression. Imbalanced regression is a well-known problem that occurs across domains, where a continuous target variable is poorly represented on ranges that are important to the end-user. Here, a re-sampling strategy is applied to modify the distribution of the target variable, biasing it towards the end-user interests so that downstream learning algorithms can be trained on the most relevant cases. The package provides an easy-to-use and extensible implementation of eight state-of-the-art re-sampling methods for regression, including four under-sampling and four over-sampling techniques. Code related to this paper is available at: https://github.com/paobranco/ImbalancedLearningRegression.

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Notes

  1. 1.

    https://pypi.org/project/ImbalancedLearningRegression/.

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Acknowledgements

We would like to thank Xinzi Hu, Lingyi Kong, and Chengen Lyu for their contributions to the re-sampling implementations.

Author information

Authors and Affiliations

  1. Faculty of Engineering, University of Ottawa, Ottawa, ON, Canada

    Wenglei Wu & Paula Branco

  2. College of Engineering, Cornell University, Ithaca, NY, USA

    Nicholas Kunz

Authors
  1. Wenglei Wu
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  2. Nicholas Kunz
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  3. Paula Branco
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Corresponding author

Correspondence to Paula Branco .

Editor information

Editors and Affiliations

  1. Grenoble Alpes University, Saint Martin d'Hères, France

    Massih-Reza Amini

  2. INSA Rouen Normandy, Saint Etienne du Rouvray, France

    Stéphane Canu

  3. Ruhr-Universität Bochum, Bochum, Germany

    Asja Fischer

  4. KU Leuven, Leuven, Belgium

    Tias Guns

  5. Central European University, Vienna, Austria

    Petra Kralj Novak

  6. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Grigorios Tsoumakas

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Wu, W., Kunz, N., Branco, P. (2023). ImbalancedLearningRegression - A Python Package to Tackle the Imbalanced Regression Problem. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13718. Springer, Cham. https://doi.org/10.1007/978-3-031-26422-1_48

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  • DOI: https://doi.org/10.1007/978-3-031-26422-1_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26421-4

  • Online ISBN: 978-3-031-26422-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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