| Refined modeling of sensor reliability in the belief function framework using contextual discounting D Mercier, B Quost, T Denœux Information fusion 9 (2), 246-258, 2008 | 222 | 2008 |
| Classifier fusion in the Dempster–Shafer framework using optimized t-norm based combination rules B Quost, MH Masson, T Denœux International Journal of Approximate Reasoning 52 (3), 353-374, 2011 | 148 | 2011 |
| CECM: Constrained evidential c-means algorithm V Antoine, B Quost, MH Masson, T Denoeux Computational Statistics & Data Analysis 56 (4), 894-914, 2012 | 94 | 2012 |
| Pairwise classifier combination using belief functions B Quost, T Denœux, MH Masson Pattern Recognition Letters 28 (5), 644-653, 2007 | 84 | 2007 |
| Clustering and classification of fuzzy data using the fuzzy EM algorithm B Quost, T Denoeux Fuzzy Sets and Systems 286, 134-156, 2016 | 51 | 2016 |
| Moving object detection and segmentation in urban environments from a moving platform D Zhou, V Frémont, B Quost, Y Dai, H Li Image and Vision Computing 68, 76-87, 2017 | 50 | 2017 |
| Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression B Quost, T Denoeux, S Li Advances in Data Analysis and Classification 11, 659-690, 2017 | 43 | 2017 |
| Contextual discounting of belief functions D Mercier, B Quost, T Denœux European Conference on Symbolic and Quantitative Approaches to Reasoning and …, 2005 | 43 | 2005 |
| CEVCLUS: evidential clustering with instance-level constraints for relational data V Antoine, B Quost, MH Masson, T Denoeux Soft Computing 18, 1321-1335, 2014 | 40 | 2014 |
| Estimation of multiple sound sources with data and model uncertainties using the EM and evidential EM algorithms X Wang, B Quost, JD Chazot, J Antoni Mechanical Systems and Signal Processing 66, 159-177, 2016 | 38 | 2016 |
| Iterative beamforming for identification of multiple broadband sound sources X Wang, B Quost, JD Chazot, J Antoni Journal of Sound and Vibration 365, 260-275, 2016 | 29 | 2016 |
| Learning from data with uncertain labels by boosting credal classifiers B Quost, T Denœux Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery From …, 2009 | 24 | 2009 |
| Classification by pairwise coupling of imprecise probabilities B Quost, S Destercke Pattern Recognition 77, 412-425, 2018 | 21 | 2018 |
| On modeling ego-motion uncertainty for moving object detection from a mobile platform D Zhou, V Frémont, B Quost, B Wang 2014 IEEE Intelligent Vehicles Symposium Proceedings, 1332-1338, 2014 | 18 | 2014 |
| One-against-all classifier combination in the framework of belief functions B Quost, T Denoeux, M Masson, A UPJV Eighth Conference on Information Fusion Conference, 356-363, 2006 | 16 | 2006 |
| Clustering fuzzy data using the fuzzy EM algorithm B Quost, T Denœux International Conference on Scalable Uncertainty Management, 333-346, 2010 | 15 | 2010 |
| Cautious weighted random forests H Zhang, B Quost, MH Masson Expert Systems with Applications 213, 118883, 2023 | 14 | 2023 |
| Adapting a combination rule to non-independent information sources B Quost, T Denoeux, MH Masson 12th Information Processing and Management of Uncertainty in Knowledge-Based …, 2008 | 12 | 2008 |
| Pairwise classifier combination in the transferable belief model B Quost, T Denaeux, M Masson 2005 7th international conference on information fusion 1, 8 pp., 2005 | 12 | 2005 |
| Combining binary classifiers with imprecise probabilities S Destercke, B Quost Integrated Uncertainty in Knowledge Modelling and Decision Making …, 2011 | 10 | 2011 |
