Learning bayesian networks in the space of orderings with estimation of distribution algorithms

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 18, Issue 4, 2004, Pages 607-625

  Romero, Txomin ^a   Larrañaga, Pedro ^a   Sierra, Basilio ^a  

^a UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

Bayesian networks; Estimation of distribution algorithms; Experimental results; Learning from data; Space of orderings

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA PROCESSING; HEURISTIC METHODS; OPTIMIZATION; PROBABILITY DISTRIBUTIONS; PROBLEM SOLVING; RANDOM PROCESSES;

BYESIAN NETWORKS; ESTIMATION OF DISTIBUTION ALGORITHMS; EXPERIMENTAL RESULTS; LEARNING FROM DATA; SPACE OF ORDERINGS;

LEARNING SYSTEMS;

EID: 3142753388     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001404003332     Document Type: Article

References (33)

1. S. Acid and L. M. de Campos, An algorithm for finding minimum d-separating sets in belief networks, Conf. Uncertainty in Artificial Intelligence, 1996, pp. 3-10.
2. S. Acid, L. M. de Campos and J. F. Huete, The search of causal orderings: a short cut for learning belief networks, European Conf. Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2001, pp. 216-227.
3. I. A. Beinlich, H. J. Suermondt, R. M. Chavez and G. F. Cooper, The ALARM monitoring system: a case study with two probabilistic inference techniques for belief networks, Second European Conf. Artificial Intelligence in Medicine, 1989, pp. 247-256.
4. E. Bengoetxea, P. Larrañaga, I. Bloch, A. Perchant and C. Boeres, Learning and simulation of Bayesian networks applied to inexact graph matching, Patt. Recogn. 35 (2002) 2867-2880.
5. R. Blanco, I. Inza and P. Larrañaga, Learning Bayesian networks from data by novel population-based, stochastic search algorithms, 9th Spanish Association for Artificial Intelligence Conf., 2001.
6. R. Blanco, I. Inza and P. Larrañaga, Learning Bayesian network structures by estimation of distribution algorithms, Int. J. Intell. Syst. 18 (2003) 205-220.
7. R. R. Bouckaert, Optimizing causal orderings for generating DAGs from data, Uncertainty in Artificial Intelligence, Vol. 8 (Morgan Kaufmann Publishers, 1992), pp. 9-16.
8. E. Castillo, J. Gutierrez and A. Hadi, Expert Systems and Probabilistic Network Models (Springer-Verlag, NY, 1997).
9. D. Chickering, D. Geiger and D. Heckerman, Learning Bayesian networks is NP-Hard, Technical Report MSR-TR-94-17 (November 1994).
10. G. F. Cooper and E. Herskovits, A Bayesian method for the induction of probabilistic networks from data, Mach. Learn. 9 (1992) 309-347.
11. Norsys Software Corp., Netica software package. http://www.norsys.com, 1998.
12. J. S. de Bonet, C. L. Isbell and P. Viola, MIMIC: finding optima by estimating probability densities, Advances in Neural Information Processing Systems, Vol. 9, 1997.
13. L. M. de Campos and J. M. Puerta, Stochastic local algorithm for learning belief networks: Searching in the space of orderings, European Conf. Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2001, pp. 228-239.
14. M. Dorigo and G. Di Caro, The ant colony optimization meta-heuristic, in New Ideas in Optimization, eds. D. Corne, M. Dorigo and F. Glover (McGraw-Hill, London, 1999), pp. 11-32.
15. N. Friedman and D. Koller, Being Bayesian about network structure. An Bayesian approach to structure discovery in Bayesian networks, Mach. Learn. 50 (2003) 95-125.
16. P. Hansen and N. Mladenovi, An introduction to variable neighborhood search, in Meta-heuristics: Advances and Trends in Local Search Paradigms for Optimization (Kluwer Academic, 1999), pp. 433-458.
17. M. Henrion, Propagating uncertainty in Bayesian networks by probabilistic logic sampling, Uncertainty in Artificial Intelligence, Vol. 2, 1988.
18. F. V. Jensen, An Introduction to Bayesian Networks (University College of London, 1996).
19. S. Kirpatrick, C. D. Gelatt and M. P. Vecchi, Optimization by simulated annealing, Science 220 (1983) 671-680.
20. P. Larrañaga, R. Etxeberria, J. A. Lozano and J. M. Peña, Optimization by learning and simulation of Bayesian and Gaussian networks, Technical Report EHU-KZAA-IK-4/99, University of the Basque Country, 1999.
21. P. Larrañaga, C. M. H. Kuijpers, R. H. Murga and Y. Yurramendi, Learning Bayesian nework structures by searching for the best ordering with genetic algorithms, IEEE Trans. Syst. Man Cybern. Part A: Syst. Humans 26 (1996) 487-493.
22. P. Larrañaga and J. A. Lozano, Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation (Kluwer Academic, 2001).
23. S. L. Lauritzen and D. J. Spiegelhalter, Local computations with probabilities on graphical structures and their application on expert systems, J. Roy. Stat. Soc. B 50 (1998) 157-224.
24. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs (Springer, Berlin, 1992).
25. H. Mühlenbein, The equation for response to selection and its use for prediction, Evolut. Comput. 5 (1998) 303-346.
26. H. Mühlenbein and H. Voigt, Gene pool recombination in genetic algorithms, in Metaheuristics: Theory and Applications, eds. J. P. Kelly and I. H. Osman (Kluwer Academic, 1996), pp. 53-62.
27. P. M. Murphy and D. W. Aha, UCI repository of machine learning databases, http://www.ics.uci.edu/mlearn/MLRepository.html, 1995.
28. J. Pearl, Probabilistic Reasoning in Intelligent Systems (Morgan Kauffmann, Palo Alto, CA, 1988).
29. J. M. Peña, J. A. Lozano and P. Larrañaga, Unsupervised learning of Bayesian networks via estimation of distribution algorithms: an application to gene expression data clustering, Int. J. Uncert. Fuzz. Knowledge-Based Syst. 12 (2003) 63-82.
30. J. M. Puerta, Local and Distributed Methods for the Induction of Statistics and Dynamics Belief Networks, Ph.D. dissertation, E. T. S de Ingenieria Informatica, Universidad de Granada, 2001 (in Spanish).
31. R. Robinson, Counting unlabelled acyclic digraphs, in Lecture Notes in Mathematics: Combinatorial Mathematics V (Springer-Verlag, 1997), pp. 28-43.
32. R. S. Shachter and C. R. Kenley, Gaussian influence diagrams, Manag. Sci. 35 (1989) 527-550.
33. D. Whitley, The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best, in Proc. 3rd Int. Conf. Genetic Algorithms, 1989, pp. 116-121.