| Influence of hyperparameters on random forest accuracy S Bernard, L Heutte, S Adam Multiple Classifier Systems: 8th International Workshop, MCS 2009, Reykjavik …, 2009 | 220 | 2009 |
| One class random forests C Désir, S Bernard, C Petitjean, L Heutte Pattern Recognition 46 (12), 3490-3506, 2013 | 195 | 2013 |
| Dynamic random forests S Bernard, S Adam, L Heutte Pattern Recognition Letters 33 (12), 1580-1586, 2012 | 159 | 2012 |
| On the selection of decision trees in random forests S Bernard, L Heutte, S Adam 2009 International joint conference on neural networks, 302-307, 2009 | 149 | 2009 |
| Using random forests for handwritten digit recognition S Bernard, S Adam, L Heutte Ninth international conference on document analysis and recognition (ICDAR …, 2007 | 131 | 2007 |
| Random forest dissimilarity based multi-view learning for radiomics application H Cao, S Bernard, R Sabourin, L Heutte Pattern Recognition 88, 185-197, 2019 | 77 | 2019 |
| Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images H Cao, S Bernard, L Heutte, R Sabourin Image Analysis and Recognition: 15th International Conference, ICIAR 2018 …, 2018 | 71 | 2018 |
| Forest-RK: A new random forest induction method S Bernard, L Heutte, S Adam Advanced Intelligent Computing Theories and Applications. With Aspects of …, 2008 | 70 | 2008 |
| The multiclass ROC front method for cost-sensitive classification S Bernard, C Chatelain, S Adam, R Sabourin Pattern Recognition 52, 46-60, 2016 | 40 | 2016 |
| Mapping fragmented agricultural systems in the Sudano-Sahelian environments of Africa using random forest and ensemble metrics of coarse resolution MODIS imagery E Vintrou, M Soumaré, S Bernard, A Bégué, C Baron, D Lo Seen Photogrammetric Engineering & Remote Sensing 78 (8), 839-848, 2012 | 33 | 2012 |
| A random forest based approach for one class classification in medical imaging C Désir, S Bernard, C Petitjean, L Heutte Machine Learning in Medical Imaging: Third International Workshop, MLMI 2012 …, 2012 | 31 | 2012 |
| A study of strength and correlation in random forests S Bernard, L Heutte, S Adam Advanced Intelligent Computing Theories and Applications: 6th International …, 2010 | 30 | 2010 |
| Towards a better understanding of random forests through the study of strength and correlation S Bernard, L Heutte, S Adam Emerging Intelligent Computing Technology and Applications. With Aspects of …, 2009 | 20 | 2009 |
| A novel random forest dissimilarity measure for multi-view learning H Cao, S Bernard, R Sabourin, L Heutte 2020 25th International Conference on Pattern Recognition (ICPR), 1344-1351, 2021 | 13 | 2021 |
| Forêts aléatoires: de l'analyse des mécanismes de fonctionnement à la construction dynamique S Bernard Université de Rouen, 2009 | 13 | 2009 |
| Dissimilarity-based representation for radiomics applications H Cao, S Bernard, L Heutte, R Sabourin First International Conference on Pattern Recognition and Artificial …, 2018 | 12 | 2018 |
| A new random forest method for one-class classification C Désir, S Bernard, C Petitjean, L Heutte Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR …, 2012 | 12 | 2012 |
| Dynamic voting in multi-view learning for radiomics applications H Cao, S Bernard, L Heutte, R Sabourin Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR …, 2018 | 8 | 2018 |
| ROC-based cost-sensitive classification with a reject option C Dubos, S Bernard, S Adam, R Sabourin 2016 23rd international conference on pattern recognition (ICPR), 3320-3325, 2016 | 5 | 2016 |
| Random forest kernel for high-dimension low sample size classification LP Cavalheiro, S Bernard, JP Barddal, L Heutte Statistics and Computing 34 (1), 9, 2024 | 4 | 2024 |
Laurent HEUTTEProfessor of Computer Science, LITIS lab, University of Rouen, FranceAdresse e-mail validée de univ-rouen.fr
