3 citations · 5 across the 2 of their papers we have counts for
9 papers
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…
Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy
Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz
We analyze the trade-off between model complexity and accuracy for random forests by breaking the trees up into individual classification rules and selecting a subset of them. We s…
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning
Yannik Klein, Michael Rapp, Eneldo Loza Mencía
Being able to model correlations between labels is considered crucial in multi-label classification. Rule-based models enable to expose such dependencies, e.g., implications, subsu…