| Decision trees for hierarchical multi-label classification C Vens, J Struyf, L Schietgat, S Džeroski, H Blockeel Machine learning 73 (2), 185, 2008 | 601 | 2008 |
| Tree ensembles for predicting structured outputs D Kocev, C Vens, J Struyf, S Džeroski Pattern Recognition 46 (3), 817-833, 2013 | 209 | 2013 |
| Ensembles of multi-objective decision trees D Kocev, C Vens, J Struyf, S Džeroski European conference on machine learning, 624-631, 2007 | 186 | 2007 |
| Predicting gene function using hierarchical multi-label decision tree ensembles L Schietgat, C Vens, J Struyf, H Blockeel, D Kocev, S Džeroski BMC bioinformatics 11 (1), 1-14, 2010 | 177 | 2010 |
| Predicting human olfactory perception from chemical features of odor molecules A Keller, RC Gerkin, Y Guan, A Dhurandhar, G Turu, B Szalai, ... Science 355 (6327), 820-826, 2017 | 115 | 2017 |
| Random forest based feature induction C Vens, F Costa 2011 IEEE 11th International Conference on Data Mining, 744-753, 2011 | 76 | 2011 |
| First order random forests: Learning relational classifiers with complex aggregates A Van Assche, C Vens, H Blockeel, S Džeroski Machine Learning 64 (1-3), 149-182, 2006 | 73 | 2006 |
| Identifying discriminative classification-based motifs in biological sequences C Vens, MN Rosso, EGJ Danchin Bioinformatics 27 (9), 1231-1238, 2011 | 65 | 2011 |
| A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes N Aghaeepour, P Chattopadhyay, M Chikina, T Dhaene, S Van Gassen, ... Cytometry Part A 89 (1), 16-21, 2016 | 51 | 2016 |
| First order random forests with complex aggregates C Vens, A Van Assche, H Blockeel, S Džeroski International Conference on Inductive Logic Programming, 323-340, 2004 | 40 | 2004 |
| Labelling strategies for hierarchical multi-label classification techniques I Triguero, C Vens Pattern Recognition 56, 170-183, 2016 | 35 | 2016 |
| A simple regression based heuristic for learning model trees C Vens, H Blockeel Intelligent Data Analysis 10 (3), 215-236, 2006 | 30 | 2006 |
| FloReMi: Flow density survival regression using minimal feature redundancy S Van Gassen, C Vens, T Dhaene, BN Lambrecht, Y Saeys Cytometry Part A 89 (1), 22-29, 2016 | 27 | 2016 |
| Refining aggregate conditions in relational learning C Vens, J Ramon, H Blockeel European Conference on Principles of Data Mining and Knowledge Discovery …, 2006 | 27 | 2006 |
| Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems K Pliakos, SH Joo, JY Park, F Cornillie, C Vens, W Van den Noortgate Computers & Education 137, 91-103, 2019 | 21 | 2019 |
| The ACE data mining system, user’s manual H Blockeel, L Dehaspe, J Ramon, J Struyf, A Van Assche, C Vens, ... Katholieke Universiteit Leuven, Belgium, 2006 | 18 | 2006 |
| Global multi-output decision trees for interaction prediction K Pliakos, P Geurts, C Vens Machine Learning 107 (8), 1257-1281, 2018 | 16 | 2018 |
| Predicting drug-target interactions with multi-label classification and label partitioning K Pliakos, C Vens, G Tsoumakas IEEE/ACM transactions on computational biology and bioinformatics, 2019 | 14 | 2019 |
| Outlier detection in relational data: A case study in geographical information systems J Maervoet, C Vens, GV Berghe, H Blockeel, P De Causmaecker Expert Systems with Applications 39 (5), 4718-4728, 2012 | 14 | 2012 |
| Machine learning for discovering missing or wrong protein function annotations FK Nakano, M Lietaert, C Vens BMC bioinformatics 20 (1), 1-32, 2019 | 13 | 2019 |
