Learning complex Bayesian network features for classification

Proceedings of the 3rd European Workshop on Probabilistic Graphical Models, PGM 2006

Volumn , Issue , 2006, Pages 9-16

  Antal, Péter ^a   Gézsi, András ^a   Hullám, Gábor ^a   Millinghoffer, András ^a  

^a BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS   (Hungary)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN APPROACHES; BAYESIAN INFERENCE; COMPLEX QUERIES; COMPUTATIONAL CHALLENGES; MARKOV BLANKETS; PRIOR KNOWLEDGE; PROBABILISTIC SEMANTICS; STRUCTURAL MODELS;

INFERENCE ENGINES; MODEL STRUCTURES; MONTE CARLO METHODS; SEMANTICS;

BAYESIAN NETWORKS;

EID: 84872163952     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. P. Antal, G. Fannes, Y. Moreau, D. Timmerman, and B. De Moor. 2004. Using literature and data to learn Bayesian networks as clinical models of ovarian tumors. AI in Med., 30:257-281. Special issue on Bayesian Models in Med.
2. W. L. Buntine. 1991. Theory refinement of Bayesian networks. In Proc. of the 7th Conf. on Uncertainty in Artificial Intelligence, pages 52-60. Morgan Kaufmann.
3. G. F. Cooper and E. Herskovits. 1992. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309-347.
4. N. Friedman and D. Koller. 2000. Being Bayesian about network structure. In Proc. of the 16th Conf. on Uncertainty in Artificial Intelligence, pages 201-211. Morgan Kaufmann.
5. N. Friedman, M. Goldszmidt, and A. Wyner. 1999. Data analysis with bayesian networks: A Bootstrap approach. In Proc. of the 15th Conf. on Uncertainty in Artificial Intelligence, pages 196-205. Morgan Kaufmann.
6. D. Gamerman. 1997. Markov Chain Monte Carlo. Chapman & Hall, London.
7. A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. 1995. Bayesian Data Analysis. Chapman & Hall, London.
8. C. Glymour and G. F. Cooper. 1999. Computation, Causation, and Discovery. AAAI Press.
9. D. Heckerman, D. Geiger, and D. Chickering. 1995. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:197-243.
10. D. Heckermann, C. Meek, and G. Cooper. 1997. A bayesian aproach to causal discovery. Technical Report, MSR-TR-97-05.
11. D. W. Hosmer and S. Lemeshow. 2000. Applied Logistic Regression. Wiley & Sons, Chichester.
12. R. Kohavi and G. H. John. 1997. Wrappers for feature subset selection. Artificial Intelligence, 97:273-324.
13. D. Koller and M. Sahami. 1996. Toward optimal feature selection. In International Conference on Machine Learning, pages 284-292.
14. D. Madigan, S. A. Andersson, M. Perlman, and C. T. Volinsky. 1996. Bayesian model averaging and model selection for markov equivalence classes of acyclic digraphs. Comm.Statist. Theory Methods, 25:2493-2520.
15. S. Mani and G. F. Cooper. 2001. A simulation study of three related causal data mining algorithms. In International Workshop on Artificial Intelligence and Statistics, pages 73-80. Morgan Kaufmann, San Francisco, CA.
16. J. Pearl. 1988. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco, CA.
17. J. Pearl. 2000. Causality: Models, Reasoning, and Inference. Cambridge University Press.
18. D. Pe'er, A. Regev, G. Elidan, and N. Friedman. 2001. Inferring subnetworks from perturbed expression profiles. Bioinformatics, Proceedings of ISMB 2001, 17(Suppl. 1):215-224.
19. C. Silverstein, S. Brin, R. Motwani, and J. D. Ullman. 2000. Scalable techniques for mining causal structures. Data Mining and Knowledge Discovery, 4(2/3):163-192.
20. D. J. Spiegelhalter and S. L. Lauritzen. 1990. Sequential updating of conditional probabilities on directed acyclic graphical structures. Networks, 20(1):579-605.
21. P. Spirtes, C. Glymour, and R. Scheines. 2001. Causation, Prediction, and Search. MIT Press.
22. I. Tsamardinos and C. Aliferis. 2003. Towards principled feature selection: Relevancy, filters, and wrappers. In Proc. of the Artificial Intelligence and Statistics, pages 334-342.