Celine Vens
Celine Vens
associate professor, Katholieke Universiteit Leuven
Verified email at kuleuven.be - Homepage
Cited by
Cited by
Decision trees for hierarchical multi-label classification
C Vens, J Struyf, L Schietgat, S Džeroski, H Blockeel
Machine learning 73 (2), 185, 2008
Tree ensembles for predicting structured outputs
D Kocev, C Vens, J Struyf, S Džeroski
Pattern Recognition 46 (3), 817-833, 2013
Ensembles of multi-objective decision trees
D Kocev, C Vens, J Struyf, S Džeroski
European conference on machine learning, 624-631, 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
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
Random forest based feature induction
C Vens, F Costa
2011 IEEE 11th International Conference on Data Mining, 744-753, 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
Identifying discriminative classification-based motifs in biological sequences
C Vens, MN Rosso, EGJ Danchin
Bioinformatics 27 (9), 1231-1238, 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
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
Labelling strategies for hierarchical multi-label classification techniques
I Triguero, C Vens
Pattern Recognition 56, 170-183, 2016
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
A simple regression based heuristic for learning model trees
C Vens, H Blockeel
Intelligent Data Analysis 10 (3), 215-236, 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
Refining aggregate conditions in relational learning
C Vens, J Ramon, H Blockeel
European Conference on Principles of Data Mining and Knowledge Discovery …, 2006
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
Global multi-output decision trees for interaction prediction
K Pliakos, P Geurts, C Vens
Machine Learning 107 (8), 1257-1281, 2018
Machine learning for discovering missing or wrong protein function annotations
FK Nakano, M Lietaert, C Vens
BMC bioinformatics 20 (1), 1-32, 2019
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
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
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