Michael Rapp
Title
Cited by
Cited by
Year
Learning Interpretable Rules for Multi-label Classification
E Loza Mencía, J Fürnkranz, E Hüllermeier, M Rapp
Explainable and Interpretable Models in Computer Vision and Machine Learning …, 2018
62018
Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules
M Rapp, EL Mencía, J Fürnkranz
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 29-42, 2018
52018
Learning Gradient Boosted Multi-label Classification Rules
M Rapp, EL Mencía, J Fürnkranz, VL Nguyen, E Hüllermeier
arXiv preprint arXiv:2006.13346, 2020
32020
Efficient Discovery of Expressive Multi-label Rules Using Relaxed Pruning
Y Klein, M Rapp, EL Mencía
International Conference on Discovery Science, 367-382, 2019
32019
Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy
M Rapp, EL Mencía, J Fürnkranz
arXiv preprint arXiv:1911.04393, 2019
22019
On the Trade-Off Between Consistency and Coverage in Multi-label Rule Learning Heuristics
M Rapp, EL Mencía, J Fürnkranz
International Conference on Discovery Science, 96-111, 2019
22019
Learning Structured Declarative Rule Sets--A Challenge for Deep Discrete Learning
J Fürnkranz, E Hüllermeier, EL Mencía, M Rapp
arXiv preprint arXiv:2012.04377, 2020
2020
A Flexible Class of Dependence-aware Multi-Label Loss Functions
E Hüllermeier, M Wever, EL Mencia, J Fürnkranz, M Rapp
arXiv preprint arXiv:2011.00792, 2020
2020
On Aggregation in Ensembles of Multilabel Classifiers
VL Nguyen, E Hüllermeier, M Rapp, EL Mencía, J Fürnkranz
International Conference on Discovery Science, 533-547, 2020
2020
Rule-Based Multi-label Classification: Challenges and Opportunities
E Hüllermeier, J Fürnkranz, EL Mencia, VL Nguyen, M Rapp
International Joint Conference on Rules and Reasoning, 3-19, 2020
2020
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Articles 1–10