Improving the scalability of optimal Bayesian network learning with external-memory frontier breadth-first branch and bound search

Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011

Volumn , Issue , 2011, Pages 479-488

  Malone, Brandon ^a   Yuan, Changhe ^a   Hansen, Eric A ^a   Bridges, Susan ^a  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; GRAPH ALGORITHMS; HEURISTIC ALGORITHMS; LEARNING ALGORITHMS; RANDOM ACCESS STORAGE; SCALABILITY;

BAYESIAN NETWORK LEARNING; BRANCH AND BOUND SEARCH; HEURISTIC SEARCH ALGORITHMS; LAYERED STRUCTURES; OPTIMAL STRUCTURES; SHORTEST PATH FINDINGS; SOLUTION RECONSTRUCTION; STATE OF THE ART;

STRUCTURAL OPTIMIZATION;

EID: 80053162012     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Chickering, D. M. 1996. Learning Bayesian networks is NP-complete. In Learning from Data: Artificial Intelligence and Statistics V,121-130. Springer-Verlag.
2. Cooper, G. F., and Herskovits, E. 1992. A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9:309-347.
3. de Campos, C. P.; Zeng, Z.; and Ji, Q. 2009. Structure learning of Bayesian networks using constraints. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, 113-120. New York, NY, USA: ACM.
4. Frank, A., and Asuncion, A. 2010. UCI machine learning repository.
5. Friedman, N.; Nachman, I.; and Peer, D. 1999. Learning Bayesian network structure from massive datasets: The "sparse candidate" algorithm. In Proceedings of UAI-13, 206-215.
6. Heckerman, D.; Geiger, D.; and Chickering, D. M. 1995. Learning Bayesian networks: The combination of knowledge and statistical data. 20:197-243.
7. Heckerman, D. 1998. A tutorial on learning with Bayesian networks. In Holmes, D., and Jain, L., eds., Innovations in Bayesian Networks, volume 156 of Studies in Computational Intelligence. Springer Berlin / Heidelberg. 33-82.
8. Jaakkola, T.; Sontag, D.; Globerson, A.; and Meila, M. 2010. Learning Bayesian network structure using LP relaxations. In Proceedings of the 13th InternationalConference on Artificial Intelligence and Statistics (AISTATS).
9. Knuth, D. E. 2009. The Art of Computer Programming, Volume 4, Fascicles 0-4. Addison-Wesley Professional, 1st edition.
10. Koivisto, M., and Sood, K. 2004. Exact Bayesian structure discovery in Bayesian networks. Journal of Machine Learning Research 549-573.
11. Korf, R.; Zhang, W.; Thayer, I.; and Hohwald, H. 2005. Frontier search. Journal of the ACM 52(5):715-748. (Pubitemid 41527577)
12. Korf, R. 2008. Linear-time disk-based implicit graph search. Journal of the ACM 35(6).
13. Malone, B.; Yuan, C.; and Hansen, E. 2011. Memoryefficient dynamic programming for learning optimal Bayesian networks. In Proceedings of the 25th national conference on Artifical intelligence.
14. Moore,A., and Lee,M. S. 1998. Cached sufficient statistics for efficient machine learning with large datasets. J. Artif. Int. Res. 8:67-91. (Pubitemid 128636194)
15. Ott, S.; Imoto, S.; and Miyano, S. 2004. Finding optimal models for small gene networks. In Pac. Symp. Biocomput, 557-567.
16. Parviainen, P., and Koivisto, M. 2009. Exact structure discovery in Bayesian networks with less space. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. Montreal, Quebec, Canada: AUAI Press.
17. Rissanen, J. 1978. Modeling by shortest data description. Automatica 14:465-471.
18. Silander, T., and Myllymaki, P. 2006. A simple approach for finding the globally optimal Bayesian network structure. In Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence (UAI-06). Arlington, Virginia: AUAI Press.
19. Singh, A., and Moore, A. 2005. Finding optimal Bayesian networks by dynamic programming. Technical report, Carnegie Mellon University.
20. Tian, J. 2000. A branch-and-bound algorithm for MDL learning Bayesian networks. In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, 580-588. Morgan Kaufmann Publishers Inc.
21. Tsamardinos, I.; Brown, L.; and Aliferis, C. 2006. The max-min hill-climbing Bayesian network structure learning algorithm. Machine learning.
22. Yuan, C.; Malone, B.; and Wu, X. 2011. Learning optimal Bayesian networks using A* search. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence.
23. Zhou, R., and Hansen, E. 2003. Sweep A*: Space-efficient heuristic search in partially ordered graphs. In Proceedings of 15th IEEE International Conf. on Tools with Artificial Intelligence, 427-434.
24. Zhou, R., and Hansen, E. A. 2006. Breadth-first heuristic search. Artificial Intelligence 170:385-408. (Pubitemid 43262057)