Bayesian network structure learning by recursive autonomy identification

Journal of Machine Learning Research

Volumn 10, Issue , 2009, Pages 1527-1570

  Yehezkel, Raanan ^a,b   Lerner, Boaz ^b  

^a Nice Systems Ltd   (Israel)

^b BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

Author keywords

Bayesian networks; Constraint based structure learning

Indexed keywords

BAYESIAN NETWORK STRUCTURE; CLASSIFICATION ACCURACY; CONDITIONAL INDEPENDENCES; CONSTRAINT-BASED; CONSTRAINT-BASED STRUCTURE LEARNING; DEPENDENCY ANALYSIS; EDGE DIRECTION; GREEDY SEARCH; HIGH ORDER; HILL CLIMBING ALGORITHMS; MAX-MIN; RUNTIMES; SEQUENTIAL APPLICATIONS; STRUCTURE DECOMPOSITION; SUB-STRUCTURES; SYNTHETIC PROBLEM; THREE PHASE; UNDIRECTED GRAPH;

ALGORITHMS; COMPUTATIONAL COMPLEXITY; DISTRIBUTED PARAMETER NETWORKS; EDUCATION; ELECTRIC CIRCUIT BREAKERS; INFERENCE ENGINES; INTELLIGENT NETWORKS; RECURSIVE FUNCTIONS; SEQUENTIAL SWITCHING; SPEECH RECOGNITION;

BAYESIAN NETWORKS;

EID: 68949163877     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (52)

1. C. F. Aliferis, I. Tsamardinos, A. Statnikov, and L. E. Brown. Causal Explorer: A causal probabilistic network learning toolkit for biomedical discovery. In Proceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences, pages 371-376, 2003.
2. S. Andreassen, F. V. Jensen, S. K. Andersen, B. Falck, U. Kjærulff, M. Woldbye, A. R. Sørensen, A. Rosenfalck, and F. Jensen. MUNIN-an expert EMG assistant. In John E. Desmedt, editor, Computer-Aided Electromyography and Expert Systems, chapter 21, pages 255-277. Elsevier Science Publishers, 1989.
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. In Proceedings of the Second European Conference on Artificial Intelligence in Medicine, pages 246-256, 1989.
4. J. Binder, D. Koller, S. Russell, and K. Kanazawa. Adaptive probabilistic networks with hidden variables. Machine Learning, 29:213-244, 1997.
5. J. Cheng. PowerConstructor system. http://www.cs.ualberta.ca/∼jcheng/ bnpc.htm, 1998.
6. J. Cheng and R. Greiner. Comparing Bayesian network classifiers. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pages 101-107, 1999.
7. J. Cheng, D. Bell, and W. Liu. Learning Bayesian networks from data: An efficient approach based on information theory. In Proceedings of the Sixth ACM International Conference on Information and Knowledge Management, pages 325-331, 1997.
8. J. Cheng, C. Hatzis, H. Hayashi, M. Krogel, S. Morishita, D. Page, and J. Sese. KDD cup 2001 report. ACM SIGKDD Explorations Newsletter, 3:47-64, 2002.
9. D.M. Chickering. Optimal structure identification with greedy search. Journal ofMachine Learning Research, 3:507-554, 2002.
10. D. M. Chickering, D. Heckerman, and C. Meek. Large-sample learning of Bayesian networks is NP-hard. Journal of Machine Learning Research, 5:1287-1330, 2004.
11. G. F. Cooper and E. A. Herskovits. Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309-347, 1992.
12. R. G. Cowell. Conditions under which conditional independence and scoring methods lead to identical selection of Bayesian network models. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pages 91-97, 2001.
13. R. G. Cowell, A. P. Dawid, S. L. Lauritzen, and D. J. Spiegelhalter. Probabilistic Networks and Expert Systems. Springer, 1999.
14. D. Dash and M. Druzdzel. A hybrid anytime algorithm for the construction of causal models from sparse sata. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pages 142-149, 1999.
15. D. Dash and M. Druzdzel. Robust independence testing for constraint-based learning of causal structure. In Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, pages 167-174, 2003.
16. J. Dem̌sar. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7:1-30, 2006.
17. T. G. Dietterich. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10:1895-1923, 1998.
18. D. Dor and M. Tarsi. A simple algorithm to construct a consistent extension of a partially oriented graph. Technical Report R-185, Cognitive Systems Laboratory, UCLA Computer Science Department, 1992.
19. M. Friedman. A comparison of alternative tests of significance for the problem of m rankings. Annals of Mathematical Statistics, 11:86-92, 1940.
20. N. Friedman, D. Geiger, and M. Goldszmidt. Bayesian network classifiers. Machine Learning, 29: 131-161, 1997.
21. N. Friedman, I. Nachman, and D. Peé;;r. Learning Bayesian network structure frommassive datasets: The "sparse-candidate" algorithm. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pages 206-215, 1999.
22. D. Grossman and P. Domingos. Learning Bayesian network classifiers by maximizing conditional likelihood. In Proceedings of the Twenty-First International Conference on Machine Learning, pages 361-368, 2004.
23. D. Heckerman. A tutorial on learning with Bayesian networks. Technical Report TR-95-06, Microsoft Research, 1995.
24. D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:197-243, 1995.
25. D. Heckerman, C.Meek, and G. F. Cooper. A Bayesian approach to causal discovery. In G. Glymour and G. Cooper, editors, Computation, Causation and Discovery, pages 141-165. AAAI Press, 1999.
26. A. Jensen and F. Jensen. MIDAS-an influence diagram for management of mildew in winter wheat. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, pages 349-356, 1996.
27. R. J. Kennett, K. Korb, and A. E. Nicholson. Seebreeze prediction using Bayesian networks. In Proceedings of the Fifth Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pages 148-153, 2001.
28. R. Kohavi and G. H. John. Wrappers for feature subset selection. Artificial Intelligence, 97:273-324, 1997.
29. ++. In Proceedings of the Sixth International Conference on Tools with AI, pages 740-743, 1994.
30. P. Kontkanen, P. Myllymaki, T. Sliander, and H. Tirri. On supervised selection of Bayesian networks. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pages 334-342, 1999.
31. K. Kristensen and I. A. Rasmussen. The use of a Bayesian network in the design of a decision support system for growing malting barley without use of pesticides. Computers and Electronics in Agriculture, 33:197-217, 2002.
32. S. Kullback and R. A. Leibler. On information and sufficiency. Annals of Mathematical Statistics, 22:79-86, 1951.
33. P. Leray and O. François. BNT structure learning package: Documentation and experiments. Technical Report FRE CNRS 2645, Laboratoire PSI, Université et INSA de Rouen, 2004.
34. M. Marengoni, C. Jaynes, A. Hanson, and E. Riseman. Ascender II, a visual framework for 3D reconstruction. In Proceedings of the First International Conference on Computer Vision Systems, pages 469-488, 1999.
35. C. Meek. Causal inference and causal explanation with background knowledge. In Proceedings of the Fifth Conference on Uncertainty in Artificial Intelligence, pages 403-410, 1995.
36. C.Meek. Graphical Models: Selecting Causal and Statistical Models. PhD thesis, CarnegieMellon University, 1997.
37. A. Moore and W. Wong. Optimal reinsertion: A new search operator for accelerated and more accurate Bayesian network structure learning. In Twentieth International Conference on Machine Learning, pages 552-559, 2003.
38. K. Murphy. The Bayes net toolbox for Matlab. Computing Science and Statistics, 33:331-350, 2001.
39. D. J. Newman, S. Hettich, C. L. Blake, and C. J. Merz. UCI repository of machine learning databases, 1998. URL http://www.ics.uci.edu/∼mlearn/ MLRepository.html.
40. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan-Kaufmann, 1988.
41. J. Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, 2000.
42. F. Pernkopf and J. Bilmes. Discriminative versus generative parameter and structure learning of Bayesian network classifiers. In Proceedings of the Twenty-Second International Conference on Machine Learning, pages 657-664, 2005.
43. T. Roos, H. Wettig, P. Grunwald, P. Myllymaki, and H. Tirri. On discriminative Bayesian network classifiers and logistic regression. Machine Learning, 59:267-296, 2005.
44. M. Singh and M. Valtorta. Construction of Bayesian network structures from data: A brief survey and an efficient algorithm. International Journal of Approximate Reasoning, 12:111-131, 1995.
45. P. Spirtes. An anytime algorithm for casual inference. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, pages 213-221, 2001.
46. P. Spirtes and C. Meek. Learning Bayesian networks with discrete variables from data. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining, pages 294-299, 1995.
47. P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction and Search. MIT Press, 2nd edition, 2000.
48. I. Tsamardinos, L. E. Brown, and C. F. Aliferis. The max-min hill-climbing Bayesian network structure learning algorithm. Machine Learning, 65:31-78, 2006a.
49. I. Tsamardinos, A. Statnikov, L. E. Brown, and C. F. Aliferis. Generating realistic large Bayesian networks by tiling. In Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference, 2006b.
50. T. Verma and J. Pearl. Equivalence and synthesis of causal models. In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, pages 220-227, 1990.
51. S. Yang and K. C. Chang. Comparison of score metrics for Bayesian network learning. IEEE Transactions on Systems, Man and Cybernetics A, 32:419-428, 2002.
52. R. Yehezkel and B. Lerner. Recursive autonomy identification for Bayesian network structure learning. In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, pages 429-436, 2005.