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In this paper, we are interested in making decisions by combining classifiers providing uncertain outputs, in the form of sets of probability distributions. More precisely, each classifier provides lower and upper bounds on the conditional probabilities of the associated classes. The classifiers are combined by computing the set of unconditional probability distributions compatible with these bounds,...
In this paper, we study how multilabel predictions can be obtained when our uncertainty is described by a convex set of probabilities. Such predictions, typically consisting of a set of potentially optimal decisions, are hard to make in large decision spaces such as the one considered in multilabel problems. However, we show that when considering the Hamming or the ranking loss, outer-approximating...
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