Bayesian Networks and Statistical Learning Applications to complex system modelling and diagnosis

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1 Bayesian Networks and Statistical Learning Applications to complex system modelling and diagnosis Philippe LERAY, Olivier François, Ahmad Faour contact: PSI (Perception, Systems and Information) Laboratory FRE CNRS 2645 Bayesian Networks and Statistical Learning Applications to complex system modelling and diagnosis p. 1/12

2 Structural learning complete data The DAG space h a super-exponential size heuristics! Constraint bed methods (IC, PC, BN-PC...) Score bed methods complete search in Tree space (MWST) greedy search in DAG space, with node ordering (K2) or without (GS) greedy search and Markov equivalence (GES) Conferences : François & Leray RJCIA 03 (french), RFIA 04 (french) Journal : JEDAI 04 (french) MWST = good performances vs. computation time MWST for GS initialisation = robust initialisation Bayesian Networks and Statistical Learning Applications to complex system modelling and diagnosis p. 2/12

3 Structural learning incomplete data Few methods deal with incomplete data Usual principle = applying EM to score bed methods greedy search in DAG space (SEM = GS+EM) Conference : François & Leray EGC05 (french) [subm. to ECSQARU 05] : MWST+EM = MWST + score estimation with EM MWST+EM for SEM initialisation = robust initialisation Perspectives : greedy search and Markov equivalence = GES+EM constraint bed methods and incomplete data Bayesian Networks and Statistical Learning Applications to complex system modelling and diagnosis p. 3/12

4 Structural learning latent variables Combinatorial explosion Where are the latent variables in the DAG? Cardinality? new operators in SEM space restriction : hierarchical latent cls model (HLC) Conference : Leray & al. ECML03 Workshop (PGM for clsification) Tree augmented HLC Perspectives : SEM+EM = dealing with incomplete data and latent variable discovery Bayesian Networks and Statistical Learning Applications to complex system modelling and diagnosis p. 4/12

5 Structural learning a priori knowledge Using a priori knowledge to simplify the search space Perspectives : Dynamic bayesian networks (2TBN) = 2 structures : intra-slice (t) and inter-slice (t t + 1) Oriented object bayesian networks (OOBN), Multi-agent bayesian networks,... Bayesian Networks and Statistical Learning Applications to complex system modelling and diagnosis p. 5/12

6 Complex system modelling and diagnosis Discovering handwriting strategies of primary school children I. Zaarour PhD thesis (completed in feb. 2004) Collaboration with a psychology lab (PSY.CO Rouen) Conferences : ECML03 Worshop - IGS03 - RFIA04 (french) Journal : IJPRAI 04 Bayesian Networks and Statistical Learning Applications to complex system modelling and diagnosis p. 6/12

7 Complex system modelling and diagnosis Intrusion detection in computer networks A. Faour PhD thesis (begin sept. 2004) Collaboration with a network security expert Conferences : EGC05 Worshop (french) Bayesian Networks and Statistical Learning Applications to complex system modelling and diagnosis p. 7/12

8 Complex system modelling and diagnosis Bayesian networks for clsification O. François PhD thesis (end envisaged in dec. 2005) Journal : RIA 2004 (french) Dysfunction detection and localisation in a chemical reactor Collaboration with a chemical process engineering lab (LRCP Rouen) Conference : SFGP 2005 (french) Micro-wave transistor thermical modelling Project with Thales Air Defense and a aero-thermochemistry lab (CORIA Rouen) financed by Haute-Normandie Region G. Mallet MSc thesis (feb-july 2005) followed by a PhD Bayesian Networks and Statistical Learning Applications to complex system modelling and diagnosis p. 8/12

9 Scientific animation French workshop on bayesian networks : June 2001 first workshop, Paris (co-organisation) March 2003 second workshop, Rouen. Jan French PGM workshop during EGC 2005 conference, Paris. Software : BNT Toolbox for Matlab code contributions responsable for structure learning package french BNT website and documentation http ://bnt.insa-rouen.fr Bayesian Networks and Statistical Learning Applications to complex system modelling and diagnosis p. 9/12

10 International activities Members of PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning) european network of excellence Collaborations Causal networks and structural learning S. Meganck & B. Manderick, Computational Modeling Lab, Vrije Universiteit Brussel (VUB), Belgium. Bayesian Networks and Statistical Learning Applications to complex system modelling and diagnosis p. 10/12

11 Selected bibliography (in english) http ://i.insa-rouen.fr/ pleray/publisrb.php International journals : Zaarour, I. et al. (2004). Clustering and bayesian network approaches for discovering handwriting strategies of primary school children. International Journal of Pattern Recognition and Artificial Intelligence, 18(7) : International conferences : Leray, P.et al. (2003). A bayesian model for discovering handwriting strategies of primary school children. In Working Notes of the Workshop on Probabilistic Graphical Models for Clsification, ECML/PKDD-2003, Zaarour, I.et al. (2003). A bayesian network model for discovering handwriting strategies of primary school children. In 11th Conference of the International Graphonomics society (IGS 2003), Misc : Leray, P. and Francois, O. (2004). BNT structure learning package : Documentation and experiments. Technical report, Laboratoire PSI. Bayesian Networks and Statistical Learning Applications to complex system modelling and diagnosis p. 11/12

12 Selected bibliography (in french) Books : Naïm, P., Wuillemin, P.-H., Leray, P., Pourret, O., and Becker, A. (2004). Réseaux bayésiens. Eyrolles, Paris. French journals : Leray, P. and Francois, O. (2004). Réseaux bayésiens pour la clsification - méthodologie et illustration dans le cadre du diagnostic médical. Revue d Intelligence Artificielle, 18/2004 : François, O. and Leray, P. (2004). Etude comparative d algorithmes d apprentissage de structure dans les réseaux bayésiens. Journal électronique d intelligence artificielle, 5(39) :1-19. French conferences : Francois, O. and Leray, P. (2005). Apprentissage de structure dans les réseaux bayésiens et données incomplètes. In Proceedings of EGC 2005 (to appear), 1-6. Faour, A. and Leray, P. (2005). Réseaux bayésiens pour le filtrage d alarmes dans les systèmes de détection d intrusion. In Proceedings of EGC 2005 Atelier Modèles graphiques probabilistes (to appear), 1-8. Francois, O. and Leray, P. (2004). Evaluation d algorithmes d apprentissage de structure pour les réseaux bayésiens. In Proceedings of 14ème Congrès Francophone Reconnaissance des Formes et Intelligence Artificielle, RFIA 2004, Bayesian Networks and Statistical Learning Applications to complex system modelling and diagnosis p. 12/12

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