| Decision trees for hierarchical multi-label classification C Vens, J Struyf, L Schietgat, S Džeroski, H Blockeel Machine learning 73, 185-214, 2008 | 851 | 2008 |
| Tree ensembles for predicting structured outputs D Kocev, C Vens, J Struyf, S Džeroski Pattern Recognition 46 (3), 817-833, 2013 | 305 | 2013 |
| 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 | 302 | 2017 |
| Ensembles of multi-objective decision trees D Kocev, C Vens, J Struyf, S Džeroski Machine Learning: ECML 2007: 18th European Conference on Machine Learning …, 2007 | 283 | 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, 1-14, 2010 | 233 | 2010 |
| 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 | 128 | 2019 |
| Identifying discriminative classification-based motifs in biological sequences C Vens, MN Rosso, EGJ Danchin Bioinformatics 27 (9), 1231-1238, 2011 | 123 | 2011 |
| Random forest based feature induction C Vens, F Costa 2011 IEEE 11th international conference on data mining, 744-753, 2011 | 117 | 2011 |
| Drug-target interaction prediction with tree-ensemble learning and output space reconstruction K Pliakos, C Vens BMC bioinformatics 21, 1-11, 2020 | 79 | 2020 |
| First order random forests: Learning relational classifiers with complex aggregates A Van Assche, C Vens, H Blockeel, S Džeroski Machine Learning 64, 149-182, 2006 | 78 | 2006 |
| Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach R Kueffner, N Zach, M Bronfeld, R Norel, N Atassi, V Balagurusamy, ... Scientific reports 9 (1), 690, 2019 | 72 | 2019 |
| 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 | 66 | 2016 |
| Labelling strategies for hierarchical multi-label classification techniques I Triguero, C Vens Pattern Recognition 56, 170-183, 2016 | 56 | 2016 |
| 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 18 (4 …, 2019 | 53 | 2019 |
| 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 | 47 | 2016 |
| First order random forests with complex aggregates C Vens, A Van Assche, H Blockeel, S Džeroski Inductive Logic Programming: 14th International Conference, ILP 2004, Porto …, 2004 | 43 | 2004 |
| Fair multi-stakeholder news recommender system with hypergraph ranking A Gharahighehi, C Vens, K Pliakos Information Processing & Management 58 (5), 102663, 2021 | 41 | 2021 |
| Machine learning for discovering missing or wrong protein function annotations: a comparison using updated benchmark datasets FK Nakano, M Lietaert, C Vens BMC bioinformatics 20, 1-32, 2019 | 38 | 2019 |
| Global multi-output decision trees for interaction prediction K Pliakos, P Geurts, C Vens Machine Learning 107, 1257-1281, 2018 | 38 | 2018 |
| Active learning for hierarchical multi-label classification FK Nakano, R Cerri, C Vens Data Mining and Knowledge Discovery 34 (5), 1496-1530, 2020 | 36 | 2020 |
