One iteration CHC algorithm for learning Bayesian networks: An effective and efficient algorithm for high dimensional problems

Progress in Artificial Intelligence

Volumn 1, Issue 4, 2012, Pages 329-346

  Gámez, José A ^a   Mateo, Juan L ^b   Puerta, José M ^a  

^a UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

^b UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

Bayesian networks; Local search; Machine learning; Scalability; Score based learning

Indexed keywords

BAYESIAN NETWORKS; ECONOMIC AND SOCIAL EFFECTS; ITERATIVE METHODS; LEARNING SYSTEMS; NP-HARD; SCALABILITY;

APPROXIMATE ALGORITHMS; COMPUTATIONAL DEMANDS; HIGH DIMENSIONAL DATASETS; HIGH-DIMENSIONAL PROBLEMS; HILL CLIMBING ALGORITHMS; LEARNING BAYESIAN NETWORKS; LOCAL SEARCH; SCORE-BASED LEARNING;

LEARNING ALGORITHMS;

EID: 84962258235     PISSN: 21926352     EISSN: 21926360     Source Type: Journal    
DOI: 10.1007/s13748-012-0033-7     Document Type: Article

References (39)

1. Acid, S., de Campos, L. M.: A hybrid methodology for learning belief networks: Benedict. Int. J. Approx. Reason. 27(3), 235-262 (2001).
2. Acid, S., de Campos, L. M.: Searching for Bayesian network structures in the space of restricted acyclic partially directed graphs. J. Artif. Intell. Res. 18, 445-490 (2003).
3. Buntine, W. L.: Theory refinement on Bayesian networks. In: Proceedings of the Seventh Annual Conference on Uncertainty in Artificial Intelligence, pp. 52-60 (1991).
4. Buntine, W. L.: A guide to the literature on learning probabilistic networks from data. IEEE Trans. Knowl. Data Eng. 8(2), 195-210 (1996).
5. Cano, R., Sordo, C., Gutiérrez, J. M.: Applications of Bayesian networks in meteorology. In: Gámez, J. A., Moral, S., Salmerón, A. (eds.) Advances in Bayesian Networks, pp. 309-327. Springer, Berlin (2004).
6. Chickering, D. M.: A transformational characterization of equivalent Bayesian network structures. In: UAI '95: Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, pp. 87-98. Morgan Kaufmann, San Francisco (1995).
7. Chickering, D. M.: Learning Bayesian networks is NP-Complete. In: Fisher, D., Lenz, H. (eds.) Learning from Data: Artificial Intelligence and Statistics, vol. V, pp. 121-130. Springer, Berlin (1996).
8. Chickering, D. M., Geiger, D., Heckerman, D.: Learning Bayesian networks: search methods and experimental results. In: Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, pp. 112-128 (1995).
9. Chickering, D. M.: Optimal structure identification with greedy search. J. Mach. Learn. Res. 3, 507-554 (2002).
10. Cooper, G. F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9, 309-347 (1992).
11. de Campos, L. M.: A scoring function for learning Bayesian networks based on mutual information and conditional independence tests. J. Mach. Learn. Res. 7, 2149-2187 (2006).
12. de Campos, L. M., Fernández-Luna, J. M., Gámez, J. A., Puerta, J. M.: Ant colony optimization for learning Bayesian networks. Int. J. Approx. Reason. 31(3), 291-311 (2002).
13. de Campos, L. M., Fernández-Luna, J. M., Puerta, J. M.: Local search methods for learning Bayesian networks using a modified neighborhood in the space of dags. In: Proceedings of IBERAMIA 2002. LNCS, vol. 2527, pp. 182-192 (2002).
14. de Campos, L. M., Puerta, J. M.: Stochastic local algorithms for learning belief networks: searching in the space of the orderings. In: 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU'01), pp. 228-239 (2001).
15. Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32(200), 675701 (1937).
16. Friedman, N., Nachman, I., Pe'er, D.: Learning Bayesian network structure from massive datasets: the "sparse candidate" algorithm. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI'99), pp. 206-215 (1999).
17. Gámez, J. A., Mateo, J. L., Puerta, J. M.: Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood. Data Mining Knowl. Discov. 22(1-2), 106-148 (2011).
18. Gámez, J. A., Puerta, J. M.: Constrained score+(local)search methods for learning Bayesian networks. In: 8th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU'05), pp. 161-173 (2005).
19. Gámez, J. A., Puerta, J. M.: Constrained score+(local)search methods for learning Bayesian networks. In: 8th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU-05). LNCS, vol. 3571, pp. 161-173 (2005).
20. Gámez, J. A., Salmerón, A., Cano, A.: Design of new algorithms for probabilistic graphical models. implementation in elvira. programo research project (tin2007-67418-c03). In: Jornada de Seguimiento de Proyectos, 2010. Programa Nacional de Tecnologías Informáticas (2010).
21. García-Pedrajas, N., de Haro-García, A.: Scaling up data mining algorithms: review and taxonomy. Prog. Artif. Intell. 1, 71-87 (2012).
22. Geiger, D., Heckerman, D., King, H., Meek, C.: Stratified exponential families: graphical models and model selection. Ann. Stat. 29(2), 505-529 (2001).
23. Haughton, D. M. A.: On the choice of a model to fit data from an exponential family. Ann. Stat. 16(1), 342-355 (1988).
24. Heckerman, D.: Bayesian networks for data mining. Data Mining Knowl. Discov. 1, 79-119 (1997).
25. Heckerman, D., Geiger, D., Chickering, D. M.: Learning Bayesian networks: the combination of knowledge and statistical data. Mach. Learn. 20(3), 197-243 (1995).
26. Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65-70 (1979).
27. Jensen, F. V., Nielsen, T. D.: Bayesian Networks and Decision Graphs, 2nd edn. Springer, Berlin (2007).
28. Larrañaga, P., Poza, M., Yurramendi, Y., Murga, R. H., Kuijpers, C. M. H.: Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters. IEEE Trans. Pattern Anal. Mach. Intell. 18(9), 912-926 (1996).
29. Nägele, A., Dejori, M., Stetter, M.: Bayesian substructure learning-approximate learning of very large network structures. In: Proceedings of the 18th European conference on Machine Learning (ECML '07), pp. 238-249 (2007).
30. Neapolitan, R.: Learning Bayesian Networks. Prentice Hall, New Jersy (2003).
31. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988).
32. Peña, J. M., Nilsson, R., Björkegren, J., Tegnér, J.: Towards scalable and data efficient learning of Markov boundaries. Int. J. Approx. Reason. 45(2), 211-232 (2006).
33. Robinson, R. W.: Counting unlabeled acyclic digraphs. In: Combinatorial Mathematics, vol. 622, pp. 28-43. Springer, Berlin (1977).
34. Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461-464 (1978).
35. Spirtes, P., Glymour, C., Scheines, R.: Causation, prediction and search. In: Lecture Notes in Statistics, vol. 81. Springer, Berlin (1993).
36. Tsamardinos, I., Brown, L. E., Aliferis, C. F.: The max-min hill-climbing Bayesian network structure learning algorithm. Mach. Learn. 65(1), 31-78 (2006).
37. van Dijk, S., van der Gaag, L. C., Thierens, D.: A skeleton-based approach to learning Bayesian networks from data. In: Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'03), pp. 132-143 (2003).
38. Verma, T., Pearl, J.: Equivalence and synthesis of causal models. In: Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence (UAI'90), pp. 255-270. Elsevier, Amsterdam (1991).
39. Wong, M. L., Leung, K.-S.: An efficient data mining method for learning Bayesian networks using an evolutionary algorithm-based hybrid approach. IEEE Trans. Evol. Comput. 8(4), 378-404 (2004).