3 citations · 5 across the 3 of their papers we have counts for
11 papers · 1 filter
Correlation-based Discovery of Disease Patterns for Syndromic Surveillance
Michael Rapp, Moritz Kulessa, Eneldo Loza Mencía +1
Early outbreak detection is a key aspect in the containment of infectious diseases, as it enables the identification and isolation of infected individuals before the disease can sp…
Gradient-based Label Binning in Multi-label Classification
Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz +1
In multi-label classification, where a single example may be associated with several class labels at the same time, the ability to model dependencies between labels is considered c…
Learning Structured Declarative Rule Sets -- A Challenge for Deep Discrete Learning
Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía +1
Arguably the key reason for the success of deep neural networks is their ability to autonomously form non-linear combinations of the input features, which can be used in subsequent…
A Flexible Class of Dependence-aware Multi-Label Loss Functions
Eyke Hüllermeier, Marcel Wever, Eneldo Loza Mencia +2
Multi-label classification is the task of assigning a subset of labels to a given query instance. For evaluating such predictions, the set of predicted labels needs to be compared…
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…