Michael Rapp
First name(s): Michael
Last name(s): Rapp

Publications of Michael Rapp sorted by journal and type

CoRR


Frontiers in Big Data


Machine Learning Journal




2018

Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier and Michael Rapp, Learning Interpretable Rules for Multi-label Classification, in: Explainable and Interpretable Models in Computer Vision and Machine Learning, pages 81--113, Springer-Verlag, 2018
[DOI]
[URL]



2021

Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz and Hüllermeier Eyke, Gradient-Based Label Binning in Multi-Label Classification, in: Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer, 2021
[URL]

2020

Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Vu-Linh Nguyen and Eyke Hüllermeier, Learning Gradient Boosted Multi-label Classification Rules, in: Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), pages 124--140, Springer, 2020
[DOI]
[URL]
Vu-Linh Nguyen, Eyke Hüllermeier, Michael Rapp, Eneldo Loza Mencía and Johannes Fürnkranz, On Aggregation in Ensembles of Multilabel Classifiers, in: Discovery Science, pages 533--547, Springer International Publishing, 2020
[DOI]
[URL]

2019

Yannik Klein, Michael Rapp and Eneldo Loza Mencía, Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning, in: Discovery Science, pages 367--382, Springer International Publishing, 2019
[DOI]
[URL]

2018

Michael Rapp, Eneldo Loza Mencía and Johannes Fürnkranz, Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules, in: PAKDD 2018: Advances in Knowledge Discovery and Data Mining, pages 29--42, Springer International Publishing, 2018
[DOI]
[URL]



2016




2019

Michael Rapp, Eneldo Loza Mencía and Johannes Fürnkranz, On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics, Knowledge Engineering Group, Technische Universität Darmstadt, number 1908.03032, ArXiv e-prints, 2019
[URL]
Michael Rapp, Eneldo Loza Mencía and Johannes Fürnkranz, Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy, Knowledge Engineering Group, Technische Universität Darmstadt, number 1911.04393, ArXiv e-prints, 2019
[URL]