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Found 3 papers, 1 papers with code
On the Calibration of Probabilistic Classifier Sets
In this paper, we extend the notion of calibration, which is commonly used to evaluate the validity of the aleatoric uncertainty representation of a single probabilistic classifier, to assess the validity of an epistemic uncertainty representation obtained by sets of probabilistic classifiers.
Set-valued prediction in hierarchical classification with constrained representation complexity
Set-valued prediction is a well-known concept in multi-class classification.
Efficient Set-Valued Prediction in Multi-Class Classification
In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee.
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