Multilabel classification via calibrated label ranking J Fürnkranz, E Hüllermeier, E Loza Mencía, K Brinker Machine learning 73 (2), 133-153, 2008 | 920 | 2008 |
Preference learning and ranking by pairwise comparison J Fürnkranz, E Hüllermeier Preference learning, 65-82, 2011 | 736 | 2011 |
Preference learning and ranking by pairwise comparison J Fürnkranz, E Hüllermeier Preference learning, 65-82, 2010 | 736 | 2010 |
Preference learning and ranking by pairwise comparison J Fürnkranz, E Hüllermeier Preference learning, 65-82, 2010 | 736 | 2010 |
Preference learning J Fürnkranz, E Hüllermeier Encyclopedia of Machine Learning, 789-795, 2010 | 736* | 2010 |
Label ranking by learning pairwise preferences E Hüllermeier, J Fürnkranz, W Cheng, K Brinker Artificial Intelligence 172 (16-17), 1897-1916, 2008 | 638 | 2008 |
Bayes optimal multilabel classification via probabilistic classifier chains K Dembczynski, W Cheng, E Hüllermeier ICML, 2010 | 559 | 2010 |
Combining instance-based learning and logistic regression for multilabel classification W Cheng, E Hüllermeier Machine Learning 76 (2), 211-225, 2009 | 483 | 2009 |
An approach to modelling and simulation of uncertain dynamical systems E Hüllermeier International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems …, 1997 | 470 | 1997 |
FURIA: an algorithm for unordered fuzzy rule induction J Hühn, E Hüllermeier Data Mining and Knowledge Discovery 19 (3), 293-319, 2009 | 459 | 2009 |
On label dependence and loss minimization in multi-label classification K Dembczyński, W Waegeman, W Cheng, E Hüllermeier Machine Learning 88 (1), 5-45, 2012 | 418* | 2012 |
Online clustering of parallel data streams J Beringer, E Hüllermeier Data & knowledge engineering 58 (2), 180-204, 2006 | 328 | 2006 |
Open challenges for data stream mining research G Krempl, I Žliobaite, D Brzeziński, E Hüllermeier, M Last, V Lemaire, ... ACM SIGKDD explorations newsletter 16 (1), 1-10, 2014 | 299 | 2014 |
Pairwise preference learning and ranking J Fürnkranz, E Hüllermeier European conference on machine learning, 145-156, 2003 | 277 | 2003 |
Fuzzy methods in machine learning and data mining: Status and prospects E Hüllermeier Fuzzy sets and Systems 156 (3), 387-406, 2005 | 259 | 2005 |
A systematic approach to the assessment of fuzzy association rules D Dubois, E Hüllermeier, H Prade Data Mining and Knowledge Discovery 13 (2), 167-192, 2006 | 233 | 2006 |
Preferences in AI: An overview C Domshlak, E Hüllermeier, S Kaci, H Prade Artificial Intelligence 175 (7-8), 1037-1052, 2011 | 218 | 2011 |
Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods E Hüllermeier, W Waegeman Machine Learning 110 (3), 457-506, 2021 | 204 | 2021 |
Grouping, overlap, and generalized bientropic functions for fuzzy modeling of pairwise comparisons H Bustince, M Pagola, R Mesiar, E Hullermeier, F Herrera IEEE Transactions on Fuzzy Systems 20 (3), 405-415, 2011 | 199 | 2011 |
Learning from ambiguously labeled examples E Hüllermeier, J Beringer Intelligent Data Analysis 10 (5), 419-439, 2006 | 183 | 2006 |