Efficient AUC Learning Curve Calculation

  • Remco R. Bouckaert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)


A learning curve of a performance measure provides a graphical method with many benefits for judging classifier properties. The area under the ROC curve (AUC) is a useful and increasingly popular performance measure. In this paper, we consider the computational aspects of calculating AUC learning curves. A new method is provided for incrementally updating exact AUC curves and for calculating approximate AUC curves for datasets with millions of instances. Both theoretical and empirical justifications are given for the approximation. Variants for incremental exact and approximate AUC curves are provided as well.


True Positive Receiver Operator Characteristic Receiver Operator Characteristic Curve Anchor Point Positive Class 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Remco R. Bouckaert
    • 1
  1. 1.Computer Science DepartmentUniversity of WaikatoNew Zealand

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