Enhancing Binary Classification by Modeling Uncertain Boundary in Three-Way Decisions
This paper presents a Support Vector Method for optimizing multivariate nonlinear performance measures like the <i>F</i><inf>1</inf>-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.
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