| Machine learning strategies for time series forecasting G Bontempi, S Ben Taieb, YAL Borgne European business intelligence summer school, 62-77, 2012 | 541 | 2012 |
| Learned lessons in credit card fraud detection from a practitioner perspective A Dal Pozzolo, O Caelen, YA Le Borgne, S Waterschoot, G Bontempi Expert systems with applications 41 (10), 4915-4928, 2014 | 416 | 2014 |
| Adaptive model selection for time series prediction in wireless sensor networks YA Le Borgne, S Santini, G Bontempi Signal Processing 87 (12), 3010-3020, 2007 | 230 | 2007 |
| New routes from minimal approximation error to principal components AA Miranda, YA Le Borgne, G Bontempi Neural Processing Letters 27 (3), 197-207, 2008 | 196 | 2008 |
| Scarff: a scalable framework for streaming credit card fraud detection with spark F Carcillo, A Dal Pozzolo, YA Le Borgne, O Caelen, Y Mazzer, ... Information fusion 41, 182-194, 2018 | 169 | 2018 |
| Combining unsupervised and supervised learning in credit card fraud detection F Carcillo, YA Le Borgne, O Caelen, Y Kessaci, F Oblé, G Bontempi Information sciences 557, 317-331, 2021 | 160 | 2021 |
| Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization F Carcillo, YA Le Borgne, O Caelen, G Bontempi International Journal of Data Science and Analytics 5 (4), 285-300, 2018 | 83 | 2018 |
| Distributed principal component analysis for wireless sensor networks YA Le Borgne, S Raybaud, G Bontempi Sensors 8 (8), 4821-4850, 2008 | 83 | 2008 |
| An adaptive modular approach to the mining of sensor network data G Bontempi, Y Le Borgne proceedings of 1st International Workshop on Data Mining in Sensor Networks …, 2005 | 75 | 2005 |
| Deep-learning domain adaptation techniques for credit cards fraud detection B Lebichot, YAL Borgne, L He-Guelton, F Oblé, G Bontempi INNS Big Data and Deep Learning conference, 78-88, 2019 | 74 | 2019 |
| Decentralised reinforcement learning for energy-efficient scheduling in wireless sensor networks M Mihaylov, YA Le Borgne, K Tuyls, A Nowé International Journal of Communication Networks and Distributed Systems 9 (3 …, 2012 | 41 | 2012 |
| Unsupervised and supervised compression with principal component analysis in wireless sensor networks Y Le Borgne, G Bontempi Proceedings of the Workshop on Knowledge Discovery from Data, 13th ACM …, 2007 | 32 | 2007 |
| Feature selection in high-dimensional dataset using MapReduce C Reggiani, YAL Borgne, G Bontempi Benelux Conference on Artificial Intelligence, 101-115, 2017 | 19 | 2017 |
| Principal component aggregation for energy efficient information extraction in wireless sensor networks YA Le Borgne, JM Dricot, G Bontempi Knowledge Discovery from Sensor Data, 2007 | 19 | 2007 |
| Une plate-forme technologique liée à la paralysie cérébrale-Le projet ICT4Rehab SVS Jan, V Wermenbol, P Van Bogaert, K Desloovere, M Degelaen, ... médecine/sciences 29 (5), 529-536, 2013 | 18* | 2013 |
| Distributed cooperation in wireless sensor networks M Mihaylov, YA Le Borgne, K Tuyls, A Nowé The 10th International Conference on Autonomous Agents and Multiagent …, 2011 | 18 | 2011 |
| Reinforcement learning for self-organizing wake-up scheduling in wireless sensor networks M Mihaylov, YAL Borgne, K Tuyls, A Nowé International Conference on Agents and Artificial Intelligence, 382-396, 2011 | 16 | 2011 |
| Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting J De Stefani, YA Le Borgne, O Caelen, D Hattab, G Bontempi International Journal of Data Science and Analytics 7 (4), 311-329, 2019 | 13 | 2019 |
| An assessment of streaming active learning strategies for real-life credit card fraud detection F Carcillo, YA Le Borgne, O Caelen, G Bontempi 2017 IEEE International Conference on Data Science and Advanced Analytics …, 2017 | 13 | 2017 |
| Bias-variance trade-off characterization in a classification problem: What differences with regression Y Le Borgne Machine Learning Group, Univ. Libre de Bruxelles, Belgium, 2005 | 12 | 2005 |
