A support vector method for multivariate performance measures

  • Authors:
  • Thorsten Joachims

  • Affiliations:
  • Cornell University, Ithaca, NY

  • Venue:
  • ICML '05 Proceedings of the 22nd international conference on Machine learning
  • Year:
  • 2005

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Abstract

This paper presents a Support Vector Method for optimizing multivariate nonlinear performance measures like the F1-score. Taking a multivariate prediction approach, we give an algorithm with which such multivariate SVMs can be trained in polynomial time for large classes of potentially non-linear performance measures, in particular ROCArea and all measures that can be computed from the contingency table. The conventional classification SVM arises as a special case of our method.