Publications


  • Book chapters
    • G. Bontempi, S. Ben Taieb, and Y. Le Borgne. Machine learning strategies for time series forecasting. In Business Intelligence, pages 62-77. Springer, 2013. [Link]
    • Y. Le Borgne and G. Bontempi. Prediction-based data collection in wireless sensor networks. In Intelligent Sensor Networks: The integration of Sensor Networks, Signal Processing and Machine Learning, pages 153-180. Taylor and Francis/CRC Press, 2012. [Link]
    • Y. Le Borgne, J.M. Dricot, and G. Bontempi. Principal component aggregation for energy-efficient information extraction in wireless sensor networks. In Knowledge Discovery from Sensor Data, pages 55-80. Taylor and Francis/CRC Press, 2008. [preprint PDF]

  • Journals
    • F. Carcillo, Y. Le Borgne, O. Caelen, and G. Bontempi. Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization. International Journal of Data Science and Analytics, 1-16, 2018.[Link]
    • F. Carcillo, A. Dal Pozzolo, Y. Le Borgne, O. Caelen, and G. Bontempi. SCARFF: A scalable framework for streaming credit card fraud detection with Spark. Information Fusion, 41(Supplement C):182 – 194, 2018.[Link]
    • A. Dal Pozzolo, O. Caelen, Y. Le Borgne, S. Waterschoot, and G. Bontempi. Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 41(10):4915 – 4928, 2014. [Preprint PDF]
    • S. Van Sint Jan, V. Wemmembol, P. Van Bogaert, K. Desloovere, M. Degelaen, B. Dan, P. Salvia, E. Ortibus, B. Bonnechère, Y. Le Borgne, G. Bontempi, S. Vansummeren, V. Sholukha, F. Moiseev, and M. Rooze. Une plate-forme technologique liée à la paralysie cérébrale. Médecine/sciences, pages 529–536, 2013.
    • M. Mihaylov, Y. Le Borgne, K. Tuyls, and A. Nowé. Decentralised reinforcement learning for energy-efficient scheduling in wireless sensor networks. International Journal of Communication Networks and Distributed Systems, 9:207-224, 2012. [PDF]
    • M. Mihaylov, Y. Le Borgne, K. Tuyls, and A. Nowé. Reinforcement learning for self-organizing wake-up scheduling in wireless sensor networks. Communications in Computer and Information Science, 271:382-397, 2012. [PDF]
    • Y. Le Borgne, S. Raybaud, and G. Bontempi. Distributed Principal Component Analysis for Wireless Sensor Networks. Sensors Journal, MDPI, Volume 8, Issue 8, August 2008, Pages 4821-4850. [Link to Sensors Journal - Open Access]
    • A. A. Miranda, Y. Le Borgne, and G. Bontempi. New Routes from Minimal Approximation Error to Principal Components. Neural Processing Letters, Springer, Volume 27, Issue 3, June 2008, Pages 197-207. [preprint PDF] [Link to springer]
    • Y. Le Borgne, S. Santini and G. Bontempi. Adaptive Model Selection for Time Series Prediction in Wireless Sensor Networks. Journal of Signal Processing, Elsevier, Volume 87, Issue 12, December 2007, Pages 3010-3020. [preprint PDF] [Link to sciencedirect]

  • Conferences
    • G. Bontempi, Y. Le Borgne and J. De Stefani. A dynamic factor machine learning method for multi-variate and multi-step-ahead forecasting. In Proceedings of the 4th IEEE International Conference on Data Science and Advanced Analytics 2017, October 2017.
    • F. Carcillo, Y. Le Borgne, O. Caelen, and G. Bontempi. An assessment of streaming active learning strategies for real-life credit card fraud detection. In Proceedings of the 4th IEEE International Conference on Data Science and Advanced Analytics 2017, October 2017.
    • Y. Le Borgne, A. Homolova, and G. Bontempi. OpenTed browser: Insights into european public spendings. In Proceedings of the European Conference on Machine Learning - SoGood workshop, September 2016.
    • G. Bontempi and Y. Le Borgne. Predictive modeling in a big data distributed setting: a scalable bias correction approach. In Proceedings of the IEEE International Congress on Big Data, July 2016.
    • C. Reggiani, Y. Le Borgne, A. Dal Pozzolo, C. Olsen, and G. Bontempi. Minimum redundancy maximum relevance: Mapreduce implementation using apache hadoop. In Proceedings of the 23rd Belgian Dutch Conference on Machine Learning (BENELEARN 2014), June 2014.
    • O. León, M.P. Cuellar, M. Delgado, Y. Le Borgne, and G. Bontempi. Human activity recognition framework in monitored environments. In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods (ICPRAM 2014), March 2014.
    • M.P. Cuellar, Y. Le Borgne, N. Galiano-Castillo, M. Arroyo, M.C. Pegalajar, Mara J. Martin- Bautista, and G. Bontempi. PRESENS: Towards smart rehabilitation with proactive sensing for remote and automatic medical evaluation. In Proceedings of the 7th International Conference on Information Systems (IADIS 2014), February 2014.
    • M.P. Cuellar, Y. Le Borgne, N. Galiano-Castillo, M. Arroyo, M.C. Pegalajar, Mara J. Martin- Bautista, and G. Bontempi. An approach for automatic evaluation of diagnosis exercises in physical therapy using depth sensors. In Proceedings of the 1st Symposium on Serious Gaming technologies, February 2014.
    • S. Van Sint Jan, B. Bonnechère, M. Foé, B. Jansen, Y. Le Borgne, F. Moiseev, L. Omelina, P.Salvia, V. Sholukha, and S. Vansummeren. The ICT4Rehab project - a fully-integrated ICT platform for patient rehabilitation including clinical database, body tracking, serious gaming and data mining. In Proceedings of the 1st Symposium on Serious Gaming technologies, February 2014.
    • Y. Le Borgne, G. Bontempi, B. Bonnechère, P. Salvia, M. Degelaen, and S. Van Sint Jan. Data mining toolbox for gait analysis in children with cerebral palsy. In Proceedings of the XXIV Congress of the International Society of Biomechanics - ISB 2013, August 2013.
    • Y. Le Borgne, B. Bonnechère, P. Salvia, S. Van Sint Jan, and G. Bontempi. Data Mining Tools for Gait Analysis of Cerebral Palsy Children (ICT4Rehab). In SOFAMEA 2013, January 2013. [PDF]
    • S. Van Sint Jan, B. De Bono, V. Sholukha, F. Moiseev, S. Vansummeren, Y. Le Borgne, G. Bontempi, B. Bonnechère, and M. Rooze. Seedep2 : Integrated modelling of the musculoskeletal system. In Virtual Physiological Human International Conference (VPH 2012), September 2012.
    • S. Van Sint Jan, G. Bontempi, and S. Van Summeren. Making the links between rehabilitation and musculoskeletal modelling: requirements and tools. In European College of Sport Science (ECSS 2012), September 2012.
    • Y. Le Borgne and G. Bontempi. Time series prediction for energy- efficient wireless sensors : Applications to environmental monitoring and video games. In Third International Conference on Sensor Systems and Software (S-Cube 2012), June 2012. [PDF]
    • M. Mihaylov, Y. Le Borgne, K. Tuyls, and A. Nowé. Decentralized (de)synchronization in wireless sensor networks. In Proceedings of the 23rd Benelux Conference on Artificial Intelligence (BNAIC 2011), Gent, Belgium, November 2011.
    • M. Devillé, Y. Le Borgne, and A. Nowé. Reinforcement learning for energy efficient routing in wireless sensor networks. In Proceedings of the 23rd Benelux Conference on Artificial Intelligence (BNAIC 2011), Gent, Belgium, November 2011.
    • Y. Le Borgne and A. Campo. Open review in computer science. Elsevier grand challenge on executable papers. In International Conference on Computational Science (ICCS 2011), Procedia Computer Science, volume 4, pages 778–780, May 2011.
    • M. Mihaylov, Y. Le Borgne, K. Tuyls, and A. Nowé. Self-organizing synchronicity and desynchronicity using reinforcement learning. In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART 2011), pages 94–103, Rome, Italy, January 2011.
    • M. Mihaylov, Y. Le Borgne, K. Tuyls, and A. Nowé. Distributed cooperation in wireless sensor networks. In Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), pages 249 – 256, May 2011.
    • M. Mihaylov, Y. Le Borgne, K. Tuyls, and A. Nowé. DESYDE: Decentralized (de)synchronization in wireless sensor networks. In Proceedings of the 20th Annual Belgian-Dutch Conference on Machine Learning (BENELEARN 2011), pages 109–110, The Hague, The Netherlands, 2011.
    • Mihaylov M., Le Borgne Y., Nowe A., and Tuyls K., "Decentralized Reinforcement Learning for Wake-up Scheduling", 7th European conference on wireless sensor networks (EWSN 2010), pp.49 - 51, 2010.
    • Le Borgne Y., Nowe A., Steenhaut K., and Bontempi G., "Demo Abstract: Demonstrating Principal Component Aggregation for Distributed Spatial Pattern Recognition", 9th International Conference on Information Processing in Sensor Networks, issue April 12, 2010 Stockholm, Sweden, pp.430 - 431, eds. Tarek Abdelzaher, Thiemo Voigt, Adam Wolisz, published by ACM, 2010.
    • Abughalieh N., Le Borgne Y., Steenhaut K., and Nowé A., "Lifetime Optimization for Wireless Sensor Networks with Correlated Data Gathering", 8th Intl. Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, pp.252 - 258, eds. Eitan Altman, Tamer Basar, Imrich Chlamtac, 2010.
    • Le Borgne Y., Nowe A., Abughalieh N., and Steenhaut K., "Distributed regression for high-level feature extraction in wireless sensor networks", 7th International Conference on Networked Sensing Systems, pp.249 - 252, eds. Hartmut Hillmer, Masateru Minami, published by IEEE, 2010.
    • J. M. Dricot, M. Van Der Haegen, Y. Le Borgne and G. Bontempi. A Modular Framework for User Localization and Tracking Using Machine Learning Techniques in Wireless Sensor Networks. Accepted at the 8th IEEE Conference on Sensors, October 2008.
    • J. M. Dricot, M. Van Der Haegen, Y. Le Borgne and G. Bontempi. Performance Evaluation of Machine Learning Technique for the Localization of Users in Wireless Sensor Networks. In L. Wehenkel and P. Geurts and R. Marée, Editors, Proceedings of the BENELEARN Machine Learning Conference, pages 93-94. 2008.
    • Y. Le Borgne and G. Bontempi. Unsupervised and Supervised Compression with Principal Component Analysis in Wireless Sensor Networks. Proceedings of the Workshop on Knowledge Discovery from Data, 13th ACM International Conference on Knowledge Discovery and Data Mining, pages 94-103. ACM Press, New York, NY, 2007.
    • Y. Le Borgne, M. Moussaid, and G. Bontempi. Simulation architecture for data processing algorithms in wireless sensor networks. Proceedings of the 20th Conference on Advanced Information Networking and Applications (AINA), pages 383–387. IEEE Press, Piscataway, NJ, 2006.
    • Y. Le Borgne, G. Bontempi. Round Robin Cycle for Predictions in Wireless Sensor Networks. Proceedings of the 2nd International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pages 253-258. IEEE Press, Piscataway, NJ, 2005.
    • G. Bontempi, Y. Le Borgne. An adaptive modular approach to the mining of sensor network data. Proceedings of the workshop on Data Mining in Sensor Networks. SIAM SDM, pages 3-9. SIAM Press, Philadelphia, PA, 2005.

  • Technical report
    • Y. Le Borgne. Bias variance trade-off characterization in a classification. What differences with regression?. Technical Report N°534, ULB, January 2005.
  • Theses
    • Y. Le Borgne, Learning in Wireless Sensor Networks for Energy-Efficient Environmental Monitoring. PhD Thesis, Université Libre de Bruxelles, 2009. [PDF]