| Decision trees for hierarchical multi-label classification C Vens, J Struyf, L Schietgat, S Džeroski, H Blockeel Machine learning 73 (2), 185-214, 2008 | 929 | 2008 |
| 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 | 379 | 2017 |
| Tree ensembles for predicting structured outputs D Kocev, C Vens, J Struyf, S Džeroski Pattern Recognition 46 (3), 817-833, 2013 | 335 | 2013 |
| Ensembles of multi-objective decision trees D Kocev, C Vens, J Struyf, S Džeroski European conference on machine learning, 624-631, 2007 | 309 | 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), 2, 2010 | 248 | 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 | 199 | 2019 |
| Identifying discriminative classification-based motifs in biological sequences C Vens, MN Rosso, EGJ Danchin Bioinformatics 27 (9), 1231-1238, 2011 | 139 | 2011 |
| Random forest based feature induction C Vens, F Costa 2011 IEEE 11th international conference on data mining, 744-753, 2011 | 138 | 2011 |
| Drug-target interaction prediction with tree-ensemble learning and output space reconstruction K Pliakos, C Vens BMC bioinformatics 21 (1), 49, 2020 | 90 | 2020 |
| 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 | 82 | 2019 |
| First order random forests: Learning relational classifiers with complex aggregates A Van Assche, C Vens, H Blockeel, S Džeroski Machine Learning 64 (1), 149-182, 2006 | 81 | 2006 |
| Online extra trees regressor SM Mastelini, FK Nakano, C Vens, ACP de Leon Ferreira IEEE Transactions on Neural Networks and Learning Systems 34 (10), 6755-6767, 2022 | 79 | 2022 |
| 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 | 76 | 2016 |
| Labelling strategies for hierarchical multi-label classification techniques I Triguero, C Vens Pattern Recognition 56, 170-183, 2016 | 65 | 2016 |
| Fair multi-stakeholder news recommender system with hypergraph ranking A Gharahighehi, C Vens, K Pliakos Information Processing & Management 58 (5), 102663, 2021 | 63 | 2021 |
| 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 | 63 | 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 | 56 | 2016 |
| Active learning for hierarchical multi-label classification FK Nakano, R Cerri, C Vens Data Mining and Knowledge Discovery 34 (5), 1496-1530, 2020 | 48 | 2020 |
| 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), 485, 2019 | 45 | 2019 |
| 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 | 45 | 2004 |
