Frénay Benoît
Frénay Benoît
Faculty of Computer Science, Université de Namur
Verified email at unamur.be - Homepage
Title
Cited by
Cited by
Year
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
9682013
Using SVMs with randomised feature spaces: an extreme learning approach.
B Frénay, M Verleysen
ESANN, 2010
1272010
Parameter-insensitive kernel in extreme learning for non-linear support vector regression
B Frénay, M Verleysen
Neurocomputing 74 (16), 2526-2531, 2011
862011
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
832013
Interpretability of machine learning models and representations: an introduction.
A Bibal, B Frénay
ESANN, 2016
792016
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
652010
A comprehensive introduction to label noise.
B Frénay, A Kabán
ESANN, 2014
612014
Is mutual information adequate for feature selection in regression?
B Frénay, G Doquire, M Verleysen
Neural Networks 48, 1-7, 2013
522013
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
472009
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
392013
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
282014
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, 2014
202014
Label noise-tolerant hidden Markov models for segmentation: application to ECGs
B Frénay, G de Lannoy, M Verleysen
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2011
172011
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
162015
Valid interpretation of feature relevance for linear data mappings
B Fránay, D Hofmann, A Schulz, M Biehl, B Hammer
2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM …, 2014
112014
On the Potential Inadequacy of Mutual Information for Feature Selection
B Frénay, G Doquire, M Verleysen
112012
Legal requirements on explainability in machine learning
A Bibal, M Lognoul, A de Streel, B Frénay
Artificial Intelligence and Law, 1-21, 2020
92020
Uncertainty and label noise in machine learning.
B Frénay
Catholic University of Louvain, Louvain-la-Neuve, Belgium, 2013
92013
Emission modelling for supervised ECG segmentation using finite differences
B Frénay, G de Lannoy, M Verleysen
4th European Conference of the International Federation for Medical and …, 2009
92009
BIR: A method for selecting the best interpretable multidimensional scaling rotation using external variables
R Marion, A Bibal, B Frénay
Neurocomputing 342, 83-96, 2019
72019
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Articles 1–20