5 papers
Skeptical inferences in multi-label ranking with sets of probabilities
Yonatan Carlos Carranza Alarcón, Vu-Linh Nguyen
In this paper, we consider the problem of making skeptical inferences for the multi-label ranking problem. We assume that our uncertainty is described by a convex set of probabilit…
Learning Gradient Boosted Multi-label Classification Rules
Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz +2
In multi-label classification, where the evaluation of predictions is less straightforward than in single-label classification, various meaningful, though different, loss functions…
On Aggregation in Ensembles of Multilabel Classifiers
Vu-Linh Nguyen, Eyke Hüllermeier, Michael Rapp +2
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 o…
Epistemic Uncertainty Sampling
Vu-Linh Nguyen, Sébastien Destercke, Eyke Hüllermeier
Various strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learne…
Reliable Multi-label Classification: Prediction with Partial Abstention
Vu-Linh Nguyen, Eyke Hüllermeier
In contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously. Thus, inst…