| 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 | 1627 | 2013 |
| Using SVMs with randomised feature spaces: an extreme learning approach. B Frénay, M Verleysen ESANN, 2010 | 142 | 2010 |
| Interpretability of machine learning models and representations: an introduction. A Bibal, B Frénay ESANN, 77-81, 2016 | 139 | 2016 |
| A comprehensive introduction to label noise. B Frénay, A Kabán ESANN, 2014 | 109 | 2014 |
| Legal requirements on explainability in machine learning A Bibal, M Lognoul, A De Streel, B Frénay Artificial Intelligence and Law 29, 149-169, 2021 | 104 | 2021 |
| 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 | 98 | 2013 |
| Parameter-insensitive kernel in extreme learning for non-linear support vector regression B Frénay, M Verleysen Neurocomputing 74 (16), 2526-2531, 2011 | 98 | 2011 |
| Is mutual information adequate for feature selection in regression? B Frénay, G Doquire, M Verleysen Neural Networks 48, 1-7, 2013 | 85 | 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 | 77 | 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 | 50 | 2009 |
| 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 | 42 | 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 | 35 | 2014 |
| 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 | 28 | 2021 |
| 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 | 27 | 2011 |
| 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 | 20 | 2015 |
| 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 | 17 | 2019 |
| 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 | 17 | 2011 |
| 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 | 15 | 2014 |
| Finding the most interpretable MDS rotation for sparse linear models based on external features. A Bibal, R Marion, B Frénay ESANN, 2018 | 14 | 2018 |
| Explaining t-SNE Embeddings Locally by Adapting LIME. A Bibal, VM Vu, G Nanfack, B Frénay ESANN, 393-398, 2020 | 12 | 2020 |
Michel VerleysenUniversité catholique de Louvain, ICTEAM, EPLVerified email at uclouvain.be
