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On Class Imbalance Correction for Classification Algorithms in Credit Scoring

In: Operations Research Proceedings 2014

Author

Listed:
  • Bernd Bischl

    (LMU München)

  • Tobias Kühn

    (LMU München)

  • Gero Szepannek

    (Stralsund University of Applied Sciences)

Abstract

Credit scoring is often modeled as a binary classification task where defaults rarely occur and the classes generally are highly unbalanced. Although many new algorithms have been proposed in the recent past to mitigate this specific problem, the aspect of class imbalance is still underrepresented in research despite its great relevance for many business applications. Within the “Machine Learning in R” (mlr) framework methods for imbalance correction are readily available and can be integrated into a systematic classifier optimization process. Different strategies are discussed, extended and compared.

Suggested Citation

  • Bernd Bischl & Tobias Kühn & Gero Szepannek, 2016. "On Class Imbalance Correction for Classification Algorithms in Credit Scoring," Operations Research Proceedings, in: Marco Lübbecke & Arie Koster & Peter Letmathe & Reinhard Madlener & Britta Peis & Grit Walther (ed.), Operations Research Proceedings 2014, edition 1, pages 37-43, Springer.
    • Handle: RePEc:spr:oprchp:978-3-319-28697-6_6
    DOI: 10.1007/978-3-319-28697-6_6
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    Cited by:

    1. Dimitrios Nikolaidis & Michalis Doumpos, 2022. "Credit Scoring with Drift Adaptation Using Local Regions of Competence," SN Operations Research Forum, Springer, vol. 3(4), pages 1-28, December.

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