| Selected publications |
⋅ Bonarini A., Bontempi G (1994). A qualitative simulation approach for fuzzy dynamical models. ACM Transactions on Modeling and Computer Simulation (TOMACS) , 4,4,258-313
⋅ Bontempi G. (1999) Local Learning Techniques for Modeling, Prediction and Control, Université Libre de Bruxelles, Belgium.
⋅ Birattari M., Bontempi G., Bersini H. (1999). Lazy learning meets the recursive least squares algorithm In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems 11, MIT Press, Cambridge, MA, pp. 375-381.
⋅ Bontempi G., Birattari M., Bersini H., (1999) Lazy learning for modeling and control design . International Journal of Control, 72, 7/8, 643-658.
⋅ Bontempi G. (2007) A blocking strategy to improve gene selection for classification of gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4,2, 293-300.
⋅ Meyer P. E. , Kontos K., Lafitte F., Bontempi G. (2007) Information-Theoretic Inference of Large Transcriptional Regulatory Networks, EURASIP Journal on Bioinformatics and Systems Biology. vol. 2007, Article ID 79879, 9 pages, 2007. doi:10.1155/2007/79879.
⋅ Le Borgne Y., Santini S., Bontempi G. (2007) Adaptive Model Selection for Time Series Prediction in Wireless Sensor Networks Signal Processing, Signal Processing. 87, 12, 3010-3020.
⋅ Haibe-Kains B., Desmedt C, Sotiriou C., Bontempi G. (2008) A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all? Bioinformatics, 24: 2137-2142.
⋅ Haibe-Kains B., Desmedt C., Rothé F., Piccart M., Sotiriou C., Bontempi G., (2010) A fuzzy gene expression-based computational approach improves breast cancer prognostication. Genome Biology 2010, 11:R18.
⋅ Bontempi G., Flauder (2015) From Dependency to Causality: A Machine Learning Approach. In JMLR Journal of Machine Learning Research, 16, 2437-2457.
⋅ Bontempi G., Ben Taieb S. (2011) Conditionally dependent strategies for multi-step-ahead prediction in local learning. International Journal of Forecasting, 27,3, 689-699.
⋅ Bontempi G., Haibe-Kains B., Desmedt C., Sotiriou C., Quackenbush J. (2011) Multiple-input multiple-output causal strategies for gene selection BMC Bioinformatics, 12,1.
⋅ Bontempi G., Birattari M., Bersini H., (1998) Recursive lazy learning for modeling and control in C. Nedellec, C. Rouveirol (Eds.) Machine Learning: ECML-98 (10th European Conference on Machine Learning) Lecture Notes in Computer Science, Springer, pp. 292-303.
⋅ Bontempi G., Birattari M., Bersini H. (1999). Local learning for iterated time-series prediction In I. Bratko and S. Dzeroski, editors, Machine Learning: Proceedings of the Sixteenth International Conference, Morgan Kaufmann Publishers, San Francisco, CA, pp. 32-38.
⋅ Bontempi G., Krujtzer W. (2002) A Data Analysis Technique for Software Performance Prediction. Proceedings of Design, Automation and Test in Europe, DATE 2002, IEEE Computer Society 2002, 971-976.
⋅ Dal Pozzolo A., Caelen O., Bontempi G. (2015) When is Undersampling Effective in Unbalanced Classification Tasks? ECML 2015, Machine Learning and Knowledge Discovery in Databases, Volume 9284 of the series Lecture Notes in Computer Science pp 200-215
⋅ Bontempi G. (2008) Long Term Time Series Prediction with Multi-Input Multi-Output Local Learning. In Proceedings of the 2nd European Symposium on Time Series Prediction (TSP), ESTSP08.
⋅ Bontempi G., Meyer P.E. (2010) Causal filter selection in microarray data. In ICML’10, International Conference On Machine Learning
⋅ Bontempi G., Caelen 0. (2011) A Selecting-the-Best Method for Budgeted Model Selection. ECML PKDD 2011, Lecture Notes in Computer Science, 2011, Volume 6911/2011, 249-262
⋅ Ben Taieb S., Bontempi G. (2011) Recursive multi-step time series forecasting by perturbing data 2011 IEEE 11th International Conference on Data Mining (ICDM), 695-704
⋅ Bontempi G. (2003) Simulating Continuous dynamical systems with uncertainty: the probability and the possibility approaches in Fuzzy Partial Differential Equations and Relational Equations. M. Nikravesh and Li A. Zadeh (eds.), Series Studies in Fuzziness and Soft Computing, Physica-Verlag, Springer.
⋅ Bontempi G (1996). Qua.Si. III: A software tool for simulation of fuzzy dynamical systems A.Javor, A. Lehmann, I. Molnar (Eds.) Modeling and Simulation ESM 96 (Proceedings European Simulation Multiconference 1996), SCS International, Ghent, Belgium, pp. 615-619.
⋅ Bontempi G., Birattari M. (1999) A bound on the cross-validation estimate for algorithm assessment, In Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC) 1999, pp. 115-122, (Best Paper Award).
⋅ Bontempi G., Birattari M. (2000) A multi-step-ahead prediction method based on local dynamic properties. in Proceedings of ESANN 2000, European Symposium on Artificial Neural Networks, Bruges, Belgium, D-Facto publ., pp.311-316.
⋅ Bontempi G., Lafruit G. (2002) Enabling multimedia QoS control with black-box modeling. I n D. Bustard, W. Liu, R. Sterritt (Eds.) Soft-Ware 2002: Computing in an Imperfect World, Lecture Notes in Computer Science, LNCS2311, 46-59.
⋅ O.Caelen, G. Bontempi (2007) Improving the exploration strategy in bandit algorithms. In Learning and Intelligent OptimizatioN LION 2007 II, Lecture Notes of Computer Science, Springer, 56-68.
⋅ Bontempi G. (2011) An optimal stopping strategy for online calibration in local search. Learning and Intelligent OptimizatioN, LION 5, Jan 17-21, 2011, Rome, Italy
|
