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The class imbalance problem

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Fig. 1: Rebalancing methods applied to a dataset with M = 50 normal and m = 5 diseased cells (IR = 10).
Fig. 2: Benefits of rebalancing classes in the training set by undersampling with increasing imbalance ratio on the performance of a CART decision tree classifier.
Fig. 3: Training with AUROC can provide acceptable performance at IR = 10 without rebalancing.
Fig. 4: Comparison of rebalancing methods on the performance of a CART decision tree classifier.

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Authors and Affiliations

  1. Farmer School of Business, Miami University, Oxford, OH, USA

    Fadel M. Megahed

  2. Department of Mathematics, University of Dayton, Dayton, OH, USA

    Ying-Ju Chen

  3. IBM Research – Almaden, San Jose, CA, USA

    Aly Megahed & Yuya Ong

  4. Department of Statistics, The Pennsylvania State University, State College, PA, USA

    Naomi Altman

  5. Canada’s Michael Smith Genome Sciences Centre, Vancouver, British Columbia, Canada

    Martin Krzywinski

Authors
  1. Fadel M. Megahed
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  2. Ying-Ju Chen
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  3. Aly Megahed
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  4. Yuya Ong
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  5. Naomi Altman
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  6. Martin Krzywinski
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Correspondence to Martin Krzywinski.

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Megahed, F.M., Chen, YJ., Megahed, A. et al. The class imbalance problem. Nat Methods 18, 1270–1272 (2021). https://doi.org/10.1038/s41592-021-01302-4

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