Approximate Reduction from AUC Maximization to 1-Norm Soft Margin Optimization

  • Daiki Suehiro
  • Kohei Hatano
  • Eiji Takimoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6925)


Finding linear classifiers that maximize AUC scores is important in ranking research. This is naturally formulated as a 1-norm hard/soft margin optimization problem over pn pairs of p positive and n negative instances. However, directly solving the optimization problems is impractical since the problem size (pn) is quadratically larger than the given sample size (p+n). In this paper, we give (approximate) reductions from the problems to hard/soft margin optimization problems of linear size. First, for the hard margin case, we show that the problem is reduced to a hard margin optimization problem over p+n instances in which the bias constant term is to be optimized. Then, for the soft margin case, we show that the problem is approximately reduced to a soft margin optimization problem over p+n instances for which the resulting linear classifier is guaranteed to have a certain margin over pairs.


Ranking Function Neural Information Processing System Positive Instance Negative Instance High AUCs 
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 2011

Authors and Affiliations

  • Daiki Suehiro
    • 1
  • Kohei Hatano
    • 1
  • Eiji Takimoto
    • 1
  1. 1.Department of InformaticsKyushu UniversityJapan

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