Publications

Journal article

• Hüllermeier, Eyke; Wever, Marcel; Loza Mencia, Eneldo; Fürnkranz, Johannes; Rapp, Michael (2022)
  A flexible class of dependence-aware multi-label loss functions
  In: Machine Learning, Vol. 111, No. 2: pp. 713-737 (full text available)
  

top

Book Section

• Rapp, Michael; Mencía, Eneldo Loza; Fürnkranz, Johannes; Nguyen, Vu-Linh; Hüllermeier, Eyke (2021)
  Learning Gradient Boosted Multi-label Classification Rules
  In: Hutter, Frank; Kersting, Kristian; Lijffijt, Jefrey; Valera, Isabel (eds.): Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part III. Lecture Notes in Computer Science; Vol. 12459. Cham: Springer. pp. 124-140
  
• Mencía, Eneldo Loza; Fürnkranz, Johannes; Hüllermeier, Eyke; Rapp, Michael (2018)
  Learning Interpretable Rules for Multi-Label Classification
  In: Jair Escalante, Hugo; Escalera, Sergio; Guyon, Isabelle; Baró, Xavier; Güçlütürk, Yağmur; Güçlü, Umut; Gerven, Marcel van (eds.): Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning; Cham: Springer. pp. 81-113
  

top

Conference Item

• Rapp, Michael; Mencía, Eneldo Loza; Fürnkranz, Johannes; Hüllermeier, Eyke (2021)
  Gradient-Based Label Binning in Multi-label Classification
  Machine Learning and Knowledge Discovery in Databases, September 13–17, 2021, Bilbao, Spain
  
• Nguyen, Vu-Linh; Hüllermeier, Eyke; Rapp, Michael; Loza Mencia, Eneldo; Fürnkranz, Johannes (2020)
  On Aggregation in Ensembles of Multilabel Classifiers
  23rd International Conference on Discovery Science, 19-21 October 2020, Virtual
  
• Hüllermeier, Eyke; Fürnkranz, Johannes; Loza Mencia, Eneldo; Nguyen, Vu-Linh; Rapp, Michael (2020)
  Rule-Based Multi-label Classification: Challenges and Opportunities
  4th International Joint Conference on Rules and Reasoning, 29 June - 1 July 2020, Virtual