Online F-Measure Optimization

Part of Advances in Neural Information Processing Systems 28 (NIPS 2015)

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Róbert Busa-Fekete, Balázs Szörényi, Krzysztof Dembczynski, Eyke Hüllermeier


The F-measure is an important and commonly used performance metric for binary prediction tasks. By combining precision and recall into a single score, it avoids disadvantages of simple metrics like the error rate, especially in cases of imbalanced class distributions. The problem of optimizing the F-measure, that is, of developing learning algorithms that perform optimally in the sense of this measure, has recently been tackled by several authors. In this paper, we study the problem of F-measure maximization in the setting of online learning. We propose an efficient online algorithm and provide a formal analysis of its convergence properties. Moreover, first experimental results are presented, showing that our method performs well in practice.