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Found 7 papers, 3 papers with code
Learning Sets of Probabilities Through Ensemble Methods
A possible approach to obtain set-valued predictions is to learn for each query instance a probability set (a. k. a.
Probabilistic Multi-Dimensional Classification
We propose a formal framework for probabilistic MDC in which learning an optimal multi-dimensional classifier can be decomposed, without loss of generality, into learning a set of (smaller) single-variable multi-class probabilistic classifiers and a directed acyclic graph.
Skeptical inferences in multi-label ranking with sets of probabilities
In this paper, we consider the problem of making skeptical inferences for the multi-label ranking problem.
Learning Gradient Boosted Multi-label Classification Rules
In multi-label classification, where the evaluation of predictions is less straightforward than in single-label classification, various meaningful, though different, loss functions have been proposed.
On Aggregation in Ensembles of Multilabel Classifiers
While a variety of ensemble methods for multilabel classification have been proposed in the literature, the question of how to aggregate the predictions of the individual members of the ensemble has received little attention so far.
Epistemic Uncertainty Sampling
In this paper, we advocate a distinction between two different types of uncertainty, referred to as epistemic and aleatoric, in the context of active learning.
Reliable Multi-label Classification: Prediction with Partial Abstention
In contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously.
