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.

本文研究了基于经验效用最大化和决策理论的方法学习最大化F-measures的分类器，分析了它们的理论基础与联系，并通过合成和真实数据集探究了它们的适用条件和优缺点，实验结果发现在具有准确模型的情况下，两种方法在大量的训练和测试集上是渐近等价的，但在实际应用中，基于经验效用最大化的方法对模型错误的鲁棒性更强，而决策理论方法更适用于处理稀有类和常见领域适应问题。

优化 F-度量值：两种方法的故事