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On Class Imbalance Correction for Classification Algorithms in Credit Scoring

  • Bernd BischlEmail author
  • Tobias KühnEmail author
  • Gero SzepannekEmail author
Conference paper
Part of the Operations Research Proceedings book series (ORP)

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.

Keywords

Random Forest Minority Class Class Imbalance Candidate Configuration Gower Distance 
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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.LMU MünchenMunichGermany
  2. 2.Stralsund University of Applied SciencesStralsundGermany

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