Dr. Michael Rapp

Research Focus

I am currently working on explainable and situation-adapted decision models for use in high-stake domains, such as healthcare or finance. This work in the field of Explainable AI is a collaborative effort together with the chair for "Organizational Behavior" of Prof. Kirsten Thommes from Paderborn university. Our joint research project is part of the TRR 318 "Constructing Explainability" (https://trr318.uni-paderborn.de/). Some of my other research interests include multi-label classification, gradient boosting, and rule-based classification models.

Research Areas

Explainable AI, Multi-label Classification, Gradient Boosting, Rule Learning

Publications

• Michael Rapp (2022)
  Multi-label Rule Learning
  Dissertation, Technical University, Darmstadt
• Eyke Hüllermeier, Marcel Wever, Eneldo Loza Mencía, Johannes Fürnkranz, and Michael Rapp (2022)
  A flexible class of dependence-aware multi-label loss functions
  In: Machine Learning 111.2, pp. 713–737
• Michael Rapp, Moritz Kulessa, Eneldo Loza Mencía, and Johannes Fürnkranz (2021)
  Correlation-based Discovery of Disease Patterns for Syndromic Surveillance
  In: Frontiers in Big Data 4
• Michael Rapp (2021)
  BOOMER – An Algorithm for Learning Gradient Boosted Multi-label Classification Rules
  In: Software Impacts 10, p. 100137
• Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, and Eyke Hüllermeier (2021)
  Gradient-based Label Binning in Multi-label Classification
  In: Proc. European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), pp. 462–477
• Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía, Vu-Linh Nguyen, and Michael Rapp (2020)
  Rule-based Multi-label Classification: Challenges and Opportunities
  In: International Joint Conference on Rules and Reasoning, pp. 3–19
• Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Vu-Linh Nguyen, and Eyke Hüllermeier (202)
  Learning Gradient Boosted Multi-label Classification Rules
  In: Proc. European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), pp. 124–140
• Vu-Linh Nguyen, Eyke Hüllermeier, Michael Rapp, Eneldo Loza Mencía, and Johannes Fürnkranz (2020)
  On Aggregation in Ensembles of Multilabel Classifiers
  In: Proc. International Conference on Discovery Science, pp. 533–547
• Yannik Klein, Michael Rapp, and Eneldo Loza Mencía (2019)
  Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning
  In: Proc. International Conference on Discovery Science, pp. 367–382
• Michael Rapp, Eneldo Loza Mencía, and Johannes Fürnkranz (2019)
  On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics
  In: Proc. International Conference on Discovery Science, pp. 96–111
• Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier, and Michael Rapp (2018)
  Learning Interpretable Rules for Multi-Label Classification
  In: Explainable and Inter- pretable Models in Computer Vision and Machine Learning, pp. 81–113
• Michael Rapp, Eneldo Loza Mencía, and Johannes Fürnkranz (2018)
  Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules
  In: Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 29–42

Conferences

• Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Melbourne, Australia, 2018
• European Summer School on Explainable Data Science (organised by the European Association for Data Science), Kirchberg, Luxembourg, 2019
• European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), Würzburg, Germany, 2019
• International Conference on Discovery Science, Split, Croatia, 2019
• European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), Ghent, Belgium, 2020
• European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), Bilbao, Spain, 2021
• Conference of the International Federation of Classification Societies (IFCS), Porto, Portugal, 2022

Teaching

• Supervision of Bachelor's and Master's theses related to my research areas.