Celine Vens
Celine Vens
associate professor, Katholieke Universiteit Leuven
Verified email at kuleuven.be - Homepage
Title
Cited by
Cited by
Year
Decision trees for hierarchical multi-label classification
C Vens, J Struyf, L Schietgat, S Džeroski, H Blockeel
Machine learning 73 (2), 185, 2008
6012008
Tree ensembles for predicting structured outputs
D Kocev, C Vens, J Struyf, S Džeroski
Pattern Recognition 46 (3), 817-833, 2013
2092013
Ensembles of multi-objective decision trees
D Kocev, C Vens, J Struyf, S Džeroski
European conference on machine learning, 624-631, 2007
1862007
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
1772010
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
1152017
Random forest based feature induction
C Vens, F Costa
2011 IEEE 11th International Conference on Data Mining, 744-753, 2011
762011
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
732006
Identifying discriminative classification-based motifs in biological sequences
C Vens, MN Rosso, EGJ Danchin
Bioinformatics 27 (9), 1231-1238, 2011
652011
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
512016
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
402004
Labelling strategies for hierarchical multi-label classification techniques
I Triguero, C Vens
Pattern Recognition 56, 170-183, 2016
352016
A simple regression based heuristic for learning model trees
C Vens, H Blockeel
Intelligent Data Analysis 10 (3), 215-236, 2006
302006
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
272016
Refining aggregate conditions in relational learning
C Vens, J Ramon, H Blockeel
European Conference on Principles of Data Mining and Knowledge Discovery …, 2006
272006
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
212019
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
182006
Global multi-output decision trees for interaction prediction
K Pliakos, P Geurts, C Vens
Machine Learning 107 (8), 1257-1281, 2018
162018
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
142019
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
142012
Machine learning for discovering missing or wrong protein function annotations
FK Nakano, M Lietaert, C Vens
BMC bioinformatics 20 (1), 1-32, 2019
132019
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