Frénay Benoît
Frénay Benoît
Faculty of Computer Science, Université de Namur
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Cited by
Cited by
Classification in the presence of label noise: a survey
B Frénay, M Verleysen
IEEE transactions on neural networks and learning systems 25 (5), 845-869, 2013
Legal requirements on explainability in machine learning
A Bibal, M Lognoul, A De Streel, B Frénay
Artificial Intelligence and Law 29, 149-169, 2021
Interpretability of machine learning models and representations: an introduction
A Bibal, B Frénay
24th european symposium on artificial neural networks, computational …, 2016
Using SVMs with randomised feature spaces: an extreme learning approach.
B Frénay, M Verleysen
ESANN, 2010
A comprehensive introduction to label noise.
B Frénay, A Kabán
ESANN, 2014
Feature selection for nonlinear models with extreme learning machines
F Benoît, M Van Heeswijk, Y Miche, M Verleysen, A Lendasse
Neurocomputing 102, 111-124, 2013
Parameter-insensitive kernel in extreme learning for non-linear support vector regression
B Frénay, M Verleysen
Neurocomputing 74 (16), 2526-2531, 2011
Is mutual information adequate for feature selection in regression?
B Frénay, G Doquire, M Verleysen
Neural Networks 48, 1-7, 2013
Clustering patterns of urban built-up areas with curves of fractal scaling behaviour
I Thomas, P Frankhauser, B Frenay, M Verleysen
Environment and Planning B: Planning and Design 37 (5), 942-954, 2010
Supervised ECG delineation using the wavelet transform and hidden Markov models
G de Lannoy, B Frénay, M Verleysen, J Delbeke
4th European Conference of the International Federation for Medical and …, 2009
Ethical adversaries: Towards mitigating unfairness with adversarial machine learning
P Delobelle, P Temple, G Perrouin, B Frénay, P Heymans, B Berendt
ACM SIGKDD Explorations Newsletter 23 (1), 32-41, 2021
Theoretical and empirical study on the potential inadequacy of mutual information for feature selection in classification
B Frénay, G Doquire, M Verleysen
Neurocomputing 112, 64-78, 2013
Estimating mutual information for feature selection in the presence of label noise
B Frénay, G Doquire, M Verleysen
Computational Statistics & Data Analysis 71, 832-848, 2014
Reinforced extreme learning machines for fast robust regression in the presence of outliers
B Frénay, M Verleysen
IEEE Transactions on Cybernetics 46 (12), 3351-3363, 2015
Constraint enforcement on decision trees: A survey
G Nanfack, P Temple, B Frénay
ACM Computing Surveys (CSUR) 54 (10s), 1-36, 2022
Ensembles of local linear models for bankruptcy analysis and prediction
L Kainulainen, Y Miche, E Eirola, Q Yu, B Frénay, E Séverin, A Lendasse
Case Studies In Business, Industry And Government Statistics 4 (2), 116-133, 2011
Decision trees: from efficient prediction to responsible AI
H Blockeel, L Devos, B Frénay, G Nanfack, S Nijssen
Frontiers in Artificial Intelligence 6, 1124553, 2023
Explaining t-SNE embeddings locally by adapting LIME
A Bibal, VM Vu, G Nanfack, B Frénay
28th European Symposium on Artificial Neural Networks, Computational …, 2020
Achieving rotational invariance with bessel-convolutional neural networks
V Delchevalerie, A Bibal, B Frénay, A Mayer
Advances in Neural Information Processing Systems 34, 28772-28783, 2021
Label noise-tolerant hidden Markov models for segmentation: application to ECGs
B Frénay, G de Lannoy, M Verleysen
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011
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