| A community effort to assess and improve drug sensitivity prediction algorithms JC Costello, LM Heiser, E Georgii, M Gönen, MP Menden, NJ Wang, ... Nature biotechnology 32 (12), 1202-1212, 2014 | 656 | 2014 |
| Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods E Hüllermeier, W Waegeman Machine Learning 110 (3), 457-506, 2021 | 516* | 2021 |
| On label dependence and loss minimization in multi-label classification K Dembczyński, W Waegeman, W Cheng, E Hüllermeier Machine Learning 88 (1-2), 5-45, 2012 | 368 | 2012 |
| Absolute quantification of microbial taxon abundances R Props, FM Kerckhof, P Rubbens, J De Vrieze, E Hernandez Sanabria, ... The ISME journal 11 (2), 584-587, 2017 | 271 | 2017 |
| Bacterial species identification from MALDI-TOF mass spectra through data analysis and machine learning K De Bruyne, B Slabbinck, W Waegeman, P Vauterin, B De Baets, ... Systematic and applied microbiology 34 (1), 20-29, 2011 | 228 | 2011 |
| An experimental comparison of cross-validation techniques for estimating the area under the ROC curve A Airola, T Pahikkala, W Waegeman, B De Baets, T Salakoski Computational Statistics & Data Analysis 55 (4), 1828-1844, 2011 | 159 | 2011 |
| An exact algorithm for F-measure maximization K Dembczynski, W Waegeman, W Cheng, E Hüllermeier Advances in neural information processing systems 24, 2011 | 149 | 2011 |
| ROC analysis in ordinal regression learning W Waegeman, B De Baets, L Boullart Pattern Recognition Letters 29 (1), 1-9, 2008 | 146 | 2008 |
| Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models S Fukuda, B De Baets, W Waegeman, J Verwaeren, AM Mouton Environmental modelling & software 47, 1-6, 2013 | 131 | 2013 |
| Optimizing the F-measure in multi-label classification: Plug-in rule approach versus structured loss minimization K Dembczynski, A Jachnik, W Kotlowski, W Waegeman, E Hüllermeier International conference on machine learning, 1130-1138, 2013 | 124 | 2013 |
| A non-linear Granger-causality framework to investigate climate–vegetation dynamics C Papagiannopoulou, DG Miralles, S Decubber, M Demuzere, ... Geoscientific Model Development 10 (5), 1945-1960, 2017 | 102 | 2017 |
| Vegetation anomalies caused by antecedent precipitation in most of the world C Papagiannopoulou, DG Miralles, WA Dorigo, NEC Verhoest, ... Environmental Research Letters 12 (7), 074016, 2017 | 101 | 2017 |
| Effects of chlorhexidine gluconate oral care on hospital mortality: a hospital-wide, observational cohort study M Deschepper, W Waegeman, K Eeckloo, D Vogelaers, S Blot Intensive care medicine 44, 1017-1026, 2018 | 100 | 2018 |
| On label dependence in multilabel classification K Dembszynski, W Waegeman, W Cheng, E Hüllermeier LastCFP: ICML Workshop on Learning from Multi-label data, 2010 | 97 | 2010 |
| A comparison of AUC estimators in small-sample studies A Airola, T Pahikkala, W Waegeman, B De Baets, T Salakoski Machine learning in systems biology, 3-13, 2009 | 83 | 2009 |
| An Analysis of Chaining in Multi-Label Classification. K Dembczynski, W Waegeman, E Hüllermeier ECAI, 294-299, 2012 | 79 | 2012 |
| An ensemble of weighted support vector machines for ordinal regression W Waegeman, L Boullart International Journal of Computer Systems Science and Engineering 3 (1), 47-51, 2009 | 71 | 2009 |
| Multi-target prediction: a unifying view on problems and methods W Waegeman, K Dembczyński, E Hüllermeier Data Mining and Knowledge Discovery 33, 293-324, 2019 | 68 | 2019 |
| Exploration and prediction of interactions between methanotrophs and heterotrophs M Stock, S Hoefman, FM Kerckhof, N Boon, P De Vos, B De Baets, ... Research in microbiology 164 (10), 1045-1054, 2013 | 68 | 2013 |
| On the bayes-optimality of f-measure maximizers W Waegeman, K Dembczyński, A Jachnik, W Cheng, E Hüllermeier Journal of Machine Learning Research 15, 3333-3388, 2014 | 67 | 2014 |
De Baets BernardGhent UniversityGeverifieerd e-mailadres voor ugent.be
