Michael Rapp | DeepAI

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Featured Co-authors

Eyke Hüllermeier
79 publications
Johannes Fürnkranz
34 publications
Marcel Wever
18 publications
Eneldo Loza Mencía
17 publications
Moritz Kulessa
7 publications
Vu-Linh Nguyen
6 publications
Yannik Klein
1 publication

∙ 10/18/2021

Correlation-based Discovery of Disease Patterns for Syndromic Surveillance

Early outbreak detection is a key aspect in the containment of infectiou...

0 Michael Rapp, et al. ∙

∙ 06/22/2021

Gradient-based Label Binning in Multi-label Classification

In multi-label classification, where a single example may be associated ...

0 Michael Rapp, et al. ∙

∙ 12/08/2020

Learning Structured Declarative Rule Sets – A Challenge for Deep Discrete Learning

Arguably the key reason for the success of deep neural networks is their...

0 Johannes Fürnkranz, et al. ∙

∙ 11/02/2020

A Flexible Class of Dependence-aware Multi-Label Loss Functions

Multi-label classification is the task of assigning a subset of labels t...

0 Eyke Hüllermeier, et al. ∙

∙ 06/23/2020

Learning Gradient Boosted Multi-label Classification Rules

In multi-label classification, where the evaluation of predictions is le...

0 Michael Rapp, et al. ∙

∙ 06/21/2020

On Aggregation in Ensembles of Multilabel Classifiers

While a variety of ensemble methods for multilabel classification have b...

0 Vu-Linh Nguyen, et al. ∙

∙ 11/11/2019

Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy

We analyze the trade-off between model complexity and accuracy for rando...

0 Michael Rapp, et al. ∙

∙ 08/19/2019

Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning

Being able to model correlations between labels is considered crucial in...

0 Yannik Klein, et al. ∙

∙ 08/08/2019

On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics

Recently, several authors have advocated the use of rule learning algori...

4 Michael Rapp, et al. ∙

∙ 12/14/2018

Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules

Exploiting dependencies between labels is considered to be crucial for m...

0 Michael Rapp, et al. ∙

∙ 11/30/2018

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in whi...

0 Eneldo Loza Mencía, et al. ∙

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