Towards more efficient multiclass AUC computations

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The area under the receiver operating characteristics curve (AUC) can be used to assess the discriminatory power of a dichotomous classifier model. Extending this measure to more than two classes is not obvious, and a number of variants have been proposed in the literature. We investigate a heuristic approximation to a method that generalizes the notion of probabilities being correctly ordered, which is equivalent to AUC, to an arbitrary number of classes. While the exact method is computationally complex, we propose a much simpler heuristic that is linear in the number of classes for every combination of data points. Using one artificial and one real-world data set, we demonstrate empirically that this simple heuristic can provide good approximations to the exact method, with Pearson correlation coefficients between 0.85 and 0.998 across all data sets.

Original languageEnglish
Title of host publication31st European Modeling and Simulation Symposium, EMSS 2019
EditorsMichael Affenzeller, Agostino G. Bruzzone, Francesco Longo, Guilherme Pereira
Number of pages5
ISBN (Electronic)9788885741263
Publication statusPublished - 2019
Event31st European Modeling and Simulation Symposium, EMSS 2019 - Lisbon, Portugal
Duration: 18 Sept 201920 Sept 2019

Publication series

Name31st European Modeling and Simulation Symposium, EMSS 2019


Conference31st European Modeling and Simulation Symposium, EMSS 2019


  • Classifier performance assessment
  • Multiclass AUC
  • Multiclass ROC


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