Extreme F-Measure Maximization using Sparse Probability Estimates

We consider the problem of (macro) F-measure maximization in the context of extreme multi-label classification (XMLC), i.e., multi-label classification with extremely large label spaces. We investigate several approaches based on recent results on the maximization of complex performance measures in binary classification. According to these results, the F-measure can be maximized by properly thresholding conditional class probability estimates. We show that a naive adaptation of this approach can be very costly for XMLC and propose to solve the problem by classifiers that efficiently deliver sparse probability estimates (SPEs), that is, probability estimates restricted to the most probable labels. Empirical results provide evidence for the strong practical performance of this approach.