  F-measures are popular performance metrics, particularly for tasks with
imbalanced data sets. Algorithms for learning to maximize F-measures follow two
approaches: the empirical utility maximization (EUM) approach learns a
classifier having optimal performance on training data, while the
decision-theoretic approach learns a probabilistic model and then predicts
labels with maximum expected F-measure. In this paper, we investigate the
theoretical justifications and connections for these two approaches, and we
study the conditions under which one approach is preferable to the other using
synthetic and real datasets. Given accurate models, our results suggest that
the two approaches are asymptotically equivalent given large training and test
sets. Nevertheless, empirically, the EUM approach appears to be more robust
against model misspecification, and given a good model, the decision-theoretic
approach appears to be better for handling rare classes and a common domain
adaptation scenario.
