Publications of Michael Rapp
2021
| , , , and , A Flexible Class of Dependence-sensitive Multi-label Loss Functions (2021), in: Machine Learning Journal |
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| , , and , Correlation-based Discovery of Disease Patterns for Syndromic Surveillance (2021), in: CoRR, abs/2110.09208 |
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| , , and , Correlation-based Discovery of Disease Patterns for Syndromic Surveillance (2021), in: Frontiers in Big Data |
| , , and , Gradient-Based Label Binning in Multi-Label Classification, in: Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer, 2021 |
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2020
| , , , and , Learning Gradient Boosted Multi-label Classification Rules, in: Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), pages 124--140, Springer, 2020 |
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| , , and , Learning Structured Declarative Rule Sets — A Challenge for Deep Discrete Learning, in: 2nd Workshop on Deep Continuous-Discrete Machine Learning (DeCoDeML), 2020 |
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| , , , and , On Aggregation in Ensembles of Multilabel Classifiers, in: Discovery Science, pages 533--547, Springer International Publishing, 2020 |
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| , , , and , Rule-Based Multi-label Classification: Challenges and Opportunities, in: Rules and Reasoning, pages 3--19, Springer International Publishing, 2020 |
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2019
| , and , Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning, in: Discovery Science, pages 367--382, Springer International Publishing, 2019 |
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| , and , On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics, in: Discovery Science, pages 96--111, Springer International Publishing, 2019 |
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| , and , 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 |
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| , and , Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy, Knowledge Engineering Group, Technische Universität Darmstadt, number 1911.04393, ArXiv e-prints, 2019 |
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2018
| , and , 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 |
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| , , and , Learning Interpretable Rules for Multi-label Classification, in: Explainable and Interpretable Models in Computer Vision and Machine Learning, pages 81--113, Springer-Verlag, 2018 |
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2016
| , A Separate-and-Conquer Algorithm for Learning Multi-Label Head Rules, TU Darmstadt, Knowledge Engineering Group, 2016 |
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