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    List of publications of Gianluca Bontempi


      Contributions to collective works (12)
  1. 9. Haibe-Kains, B., Desmedt, C., Loi, S., Delorenzi, M., Sotiriou, C., & Bontempi, G. (2008). Computational Intelligence in Clinical Oncology : lessons learned from an analysis of a clinical study. In T. G. Smolinsk, M. G. Milanova, & A.-E. Hassanien (Eds.), Applications of Computational Intelligence in Biology - Current Trends and Open Problems (1 ed., pp. 237-268). Springer-Verlag.(Studies in Computational Intelligence, 122).
  2. 10. Le Borgne, Y.-A., Dricot, J.-M., & Bontempi, G. (2008). Principal component aggregation for energy efficient information extraction in wireless sensor networks. In Knowledge Discovery from Sensor Data (pp. 55-80). Boca Raton: Taylor and Francis/CRC Press.
  3. 11. Bontempi, G. (2004). Simulating Continuous dynamical systems with uncertainty: the probability and the possibility approaches. In M. Nikravesh & L. A. Zadeh (Eds.), Fuzzy Partial Differential Equations and Relational Equations (pp. 130-151). Physica-Verlag.(Series Studies in Fuzziness and Soft Computing, 142).
  4. 12. Bontempi, G., Birattari, M., & Bersini, H. (2001). Lazy Learning: a local method for supervised learning. In L. C. Jain & J. J. Kacprzyk (Eds.), New Learning Paradigms in Soft Computing (pp. 97-137). Heidelberg: Physica-Verlag.
    5.   Peer-reviewed journal articles (156)
  5. 1. Simar, C., Colot, M., Cebolla Alvarez, A. M., Petieau, M., Chéron, G., & Bontempi, G. (2024). Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality. Frontiers in Neuroscience, 18. doi:10.3389/fnins.2024.1329411
  6. 2. Cerqueira, V., Torgo, L., & Bontempi, G. (2024). Instance-based meta-learning for conditionally dependent univariate multi-step forecasting. International journal of forecasting. doi:10.1016/j.ijforecast.2023.12.010
  7. 3. Lebichot, B., Siblini, W., Paldino, G. M., Le Borgne, Y.-A., Oblé, F., & Bontempi, G. (2024). Assessment of catastrophic forgetting in continual credit card fraud detection. Expert systems with applications, 249, 123445. doi:10.1016/j.eswa.2024.123445
  8. 4. Bertolucci Coelho, L., Torres Justo, D., Vangrunderbeek, V., Bernal, M., Paldino, G. M., Bontempi, G., & Ustarroz Troyano, J. (2023). Estimating pitting descriptors of 316 L stainless steel by machine learning and statistical analysis. npj Materials degradation, 7(1). doi:10.1038/s41529-023-00403-z
  9. 5. Muñoz Salamanca, E., Dave, H., D'Alessio, G., Bontempi, G., Parente, A., & Le Clainche, S. (2023). Extraction and analysis of flow features in planar synthetic jets using different machine learning techniques. Physics of fluids, 35. doi:https://doi.org/10.1063/5.0163833
  10. 6. Bertolucci Coelho, L., Torres Morillo, D., Bernal, M., Paldino, G. M., Bontempi, G., & Ustarroz Troyano, J. (2023). Probing the randomness of the local current distributions of 316 L stainless steel corrosion in NaCl solution. Corrosion science, 217, 111104. doi:10.1016/j.corsci.2023.111104
  11. 7. Verhelst, T., Mercier, D., Shrestha, J., & Bontempi, G. (2023). Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment. Machine learning. doi:10.1007/s10994-023-06317-w
  12. 8. Lunghi, D., Paldino, G. M., Caelen, O., & Bontempi, G. (2023). An Adversary Model of Fraudsters’ Behavior to Improve Oversampling in Credit Card Fraud Detection. IEEE access, 11, 136666-136679. doi:10.1109/ACCESS.2023.3337635
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