Celine Vens
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Decision trees for hierarchical multi-label classification
C Vens, J Struyf, L Schietgat, S Džeroski, H Blockeel
Machine learning 73, 185-214, 2008
Tree ensembles for predicting structured outputs
D Kocev, C Vens, J Struyf, S Džeroski
Pattern Recognition 46 (3), 817-833, 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
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
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
Identifying discriminative classification-based motifs in biological sequences
C Vens, MN Rosso, EGJ Danchin
Bioinformatics 27 (9), 1231-1238, 2011
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
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, 149-182, 2006
Drug-target interaction prediction with tree-ensemble learning and output space reconstruction
K Pliakos, C Vens
BMC bioinformatics 21, 1-11, 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
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
Labelling strategies for hierarchical multi-label classification techniques
I Triguero, C Vens
Pattern Recognition 56, 170-183, 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
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
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
Fair multi-stakeholder news recommender system with hypergraph ranking
A Gharahighehi, C Vens, K Pliakos
Information Processing & Management 58 (5), 102663, 2021
Global multi-output decision trees for interaction prediction
K Pliakos, P Geurts, C Vens
Machine Learning 107, 1257-1281, 2018
A simple regression based heuristic for learning model trees
C Vens, H Blockeel
Intelligent Data Analysis 10 (3), 215-236, 2006
Active learning for hierarchical multi-label classification
FK Nakano, R Cerri, C Vens
Data Mining and Knowledge Discovery 34 (5), 1496-1530, 2020
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