2021
Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz and Hüllermeier Eyke, Gradient-Based Label Binning in Multi-Label Classification, in: Proceedings of the European Conference of Machine Learning (ECML2021), 2021
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2020
Eyke Hüllermeier, Johannes Fürnkranz and Eneldo Loza Mencía, Conformal Rule-Based Multi-label Classification, in: KI 2020: Advances in Artificial Intelligence, Springer, Cham, 2020
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Simon Bohlender, Eneldo Loza Mencía and Moritz Kulessa, Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains, Knowledge Engineering Group, Technische Universität Darmstadt, number 2006.08094 [cs.LG], ArXiv e-prints, 2020
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Simon Bohlender, Eneldo Loza Mencía and Moritz Kulessa, Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains, in: Discovery Science - 23rd International Conference, {DS} 2020, Thessaloniki, Greece, October 19-21, 2020, Proceedings, pages 471--485, Springer International Publishing, 2020
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Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Vu-Linh Nguyen and Eyke Hüllermeier, Learning Gradient Boosted Multi-label Classification Rules, in: Proceedings of the European Conference of Machine Learning (ECML2020), Springer, 2020
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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
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Vu-Linh Nguyen and Eyke Hüllermeier, Reliable Multilabel Classification: Prediction with Partial Abstention (2020), in: Proceedings of the AAAI Conference on Artificial Intelligence, 34:04(5264-5271)
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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
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Jinseok Nam, Young{-}Bum Kim, Eneldo Loza Mencía, Sunghyun Park, Ruhi Sarikaya and Johannes Fürnkranz, Learning Context-dependent Label Permutations for Multi-label Classification, in: Proceedings of the 36th International Conference on Machine Learning (ICML-19), pages 4733--4742, {PMLR}, 2019
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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
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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
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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
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2016