Convex Calibrated Surrogates for the Multi-Label F-Measure.

ABSTRACT: The F-measure is a widely used performance measure for multi-label classification, where multiple labels can be active in an instance simultaneously (e.g. in image tagging, multiple tags can be active in any image). In particular, the F-measure explicitly balances recall (fraction of active labels predicted to be active) and precision (fraction of labels predicted to be active that are actually so), both of which are important in evaluating the overall performance of a multi-label classifier. As with most discrete prediction problems, however, directly optimizing the F-measure is computationally hard. In this paper, we explore the question of designing convex surrogate losses that are calibrated for the F-measure - specifically, that have the property tha

SUBMITTER: Zhang M 

PROVIDER: S-EPMC8276679 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature