3 citations · 5 across the 4 of their papers we have counts for
14 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…
Revisiting Non-Specific Syndromic Surveillance
Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz
Infectious disease surveillance is of great importance for the prevention of major outbreaks. Syndromic surveillance aims at developing algorithms which can detect outbreaks as ear…
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…
Conformal Rule-Based Multi-label Classification
Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencia
We advocate the use of conformal prediction (CP) to enhance rule-based multi-label classification (MLC). In particular, we highlight the mutual benefit of CP and rule learning: Rul…