An iterated local search algorithm for learning Bayesian networks with restarts based on conditional independence tests

International Journal of Intelligent Systems

Volumn 18, Issue 2, 2003, Pages 221-235

  De Campos, Luis M ^a   Fernández Luna, Juan M ^b   Puerta, J Miguel ^c  

^a UNIVERSITY OF GRANADA   (Spain)

^b UNIVERSITY OF JAÉN   (Spain)

^c UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

GRAPH THEORY; KNOWLEDGE BASED SYSTEMS; KNOWLEDGE REPRESENTATION; LEARNING ALGORITHMS; MATHEMATICAL MODELS; NEURAL NETWORKS; PROBABILITY DISTRIBUTIONS; RANDOM PROCESSES; STATISTICAL TESTS;

ARC ADDITION; ARC DELETION; BAYESIAN NETWORKS; CONDITIONAL INDEPENDENCE TESTS; DIRECTED ACYCLIC GRAPH; GREEDY HILL CLIMBING METHOD; ITERATED LOCAL SEARCH ALGORITHM;

LEARNING SYSTEMS;

EID: 0037320072     PISSN: 08848173     EISSN: None     Source Type: Journal    
DOI: 10.1002/int.10085     Document Type: Article

References (19)

1. Jensen, FV. An introduction to Bayesian networks. London: UCL Press, 1996.
2. Pearl, J. Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo, CA: Morgan Kaufmann; 1988.
3. de Campos, LM. Independency relationships and learning algorithms for singly connected networks. J Exp Theor Artif Intell 1998; 10(4):511-549.
4. de Campos, LM, Huete, JF. A new approach for learning belief networks using independence criteria. Int J Approx Reason 2000,24:11-37.
5. Pearl, J, Verma, TS. Equivalence and synthesis of causal models. In: Proc 6th Conf on Uncertainty in Artificial Intelligence. 1990. pp 220-227.
6. Spirtes, P, Glymour, C, Scheines, R. Causation, prediction, and search. Lecture Notes in Statistics 81. Berlin: Springer Verlag; 1993.
7. Cooper, GF, Herskovits, E. A Bayesian method for the induction of probabilistic networks from data. Machine Learning 1992;9(4):309-348.
8. Heckerman, D, Geiger, D, Chickering, DM. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 1995;20:197-244.
9. Lam, W, Bacchus, F. Learning Bayesian belief networks. An approach based on the MDL principle. Computational Intell 1994; 10(4):269-293.
10. Acid, S, de Campos, LM. A hybrid methodology for learning belief networks: Benedict. Int J Approx Reason 2001;27(3):235-262.
11. Dash, D, Druzdel, M. A hybrid anytime algorithm for the construction of causal models from sparse data. In: Proc 15th Conf on Uncertainty in Artificial Intelligence. San Mateo, CA: Morgan Kaufmann 1999, pp 142-149.
12. Singh, M, Valtorta, M. Construction of Bayesian network structures from data: A brief survey and an efficient algorithm. Int J Approx Reason 1995; 12:111-131.
13. Chickering, DM, Geiger, D, Heckerman, D. Learning Bayesian networks is NP-Complete. In: Fisher D, Lenz H, editors. Learning from Data: Artificial Intelligence and Statistic V. Berlin: Springer-Verlag; 1996. pp 121-130.
14. Beinlich, IA, Suermondt, HJ, Chavez, RM, Cooper, GF. The ALARM monitoring system: A case study with two probabilistic inference techniques for belief network. In: Proc 2nd European Conf on Artificial Intelligence in Medicine. Berlin: Springer-Verlag; 1989. pp 247-256.
15. Binder, J, Koller, D, Russel, S, Kanazawa, K. Adaptive probabilistic networks with hidden variables. Machine Learning 1997;29(2):213-244.
16. Acid, S, de Campos, LM. An algorithm for finding minimum d-separating sets in belief networks, In: Proc 12th Conf Uncertainty in Artificial Intelligence. San Mateo, CA: Morgan Kaufmann 1996. pp 3-10.
17. Kocka, T, Castelo, R. Improved learning of Bayesian networks. In: Proc 17th Conf on Uncertainty in Artificial Intelligence. San Mateo, CA: Morgan Kaufmann 2001. pp 269-276.
18. Larrañaga, P, Poza, M, Yurramendi, Y, Murga, R, Kuijpers, C. Structure learning of Bayesian networks by genetic algorithms: A performance analysis of control parameters. IEEE Trans Pattern Anal Mach Intell 1996;18(9):912-926.
19. de Campos, LM, Puerta, JM. Stochastic local search algorithms for learning belief networks: Searching in the space of orderings. Lecture Notes in Artificial Intelligence, vol 2143, Berlin: Springer-Verlag; 2001. pp 228-239.