A novel hybrid evolutionary algorithm for learning Bayesian networks from incomplete data

2006 IEEE Congress on Evolutionary Computation, CEC 2006

Volumn , Issue , 2006, Pages 916-923

  Guo, Yuan Yuan ^b   Wong, Man Leung ^a,c   Cai, Zhi Hua ^b  

^a IEEE   (Hong Kong)

^b CHINA UNIVERSITY OF GEOSCIENCES   (China)

^c LINGNAN UNIVERSITY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

GREEDY HILL CLIMBING SEARCH METHOD; INCOMPLETE DATA; STATE OF THE ART LEARNING ALGORITHMS; STRUCTURAL EXPECTATION MAXIMIZATION (EM);

BAYESIAN NETWORKS; DATA PROCESSING; DATA STRUCTURES; LEARNING ALGORITHMS; LEARNING SYSTEMS; OPTIMIZATION;

EVOLUTIONARY ALGORITHMS;

EID: 34547317077     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. W. Lam and F. Bacchus, "Learning Bayesian belief networks-an approach based on the MDL principle," Computer Intelligence, vol. 10, no. 4, pp. 269-293, 1994.
2. M. L. Wong and K. S. Leung, "An Efficient Data Mining Method for Learning Bayesian Networks Using an Evolutionary Algorithm-Based Hybrid Approach," IEEE Transactions on Evolutionary Computation, vol. 8, no. 4, pp. 378-404, Aug. 2004.
3. N. Friedman, "Learning Belief Networks in the Presence of Missing Values and Hidden Variables," In ML'97, 1997.
4. N. Friedman, "The Bayesian Structural EM Algorithm," In UAI, 1998.
5. J. W. Myers, K. B. Laskey and K. A. DeJong, "Learning Bayesian Networks from Incomplete Data Using Evolutionary Algorithms," In GECCO'99, 1999.
6. A. P. Dempster, N. M. Laird and D. B. Rubin, "Maximum. Likelihood from Incomplete Data via the EM Algorithm," Journal of the Royal Statistical Society(B), vol. 39, no. 1, pp. 1-38, 1977.
7. S. L. Lauritzen, "The EM algorithm for graphical association models with missing data," Computational Statistics and Data Analysis, pp.191-201, 1995.
8. J. M. Peña, J. A. Lozano and P. Larrañaga, "An improved Bayesian Structural EM algorithm for learning Bayesian networks for clustering," Pattern. Recognition Letters, pp. 779-786, 2000.
9. J. M. Peña, J. A. Lozano and P. Larrañaga, "Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction," Machine Learning(2002), pp. 63-89, April, 2002.
10. M. Ramoni and P. Sebastinani, "Parameter Estimation in Bayesian Networks from Incomplete Databases," Intelligent Data Analysis 2(1), 1998.
11. M. Ramoni and P. Sebastinani, "Efficient Parameter Learning in Bayesian. networks from incomplete databases," KMI-TR-41, 1997.
12. M. Ramoni and P. Sebastinani, "The use of exogenous knowledge to learn Bayesian networks from incomplete databases," KMI-TR-44, 1997.
13. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, "Numerical Recipes in C: The Art of Scientific Computing," 2nd ed., pp. 423, Cambridge Univ. Press, 1992.
14. D. Heckerman, "A Tutorial on Learning Bayesian Networks," Microsoft Res., Adv. Technol. Div., Redmond, WA, MSR-TR-95-06, 1995.
15. Microsoft Bayesian. Network Editor and Toolkit (Online). Available: http://research.microsoft.com/adapt/MSBNx/.
16. C. Huang, and A. Darwinche, "Inference in belief networks: a procedural guide," International Journal of Approximate Reasoning, vol. 15, no. 3, pp. 225-263, 1996.
17. Norsys Bayes Net Library (Online). Available: http://www.no.rsys.com/ net_library.htm.
18. D. Chickering, "Learning Equivalence Classes of Bayesian Network Structures," Journal of Machine Learning Research 2(2002), pp. 445-498, 2002.
19. D. Chickering, "The WinMine Toolkit," Microsoft Res., Adv. Technol. Div., Redmond, WA, MSR-TR-2002-103, 2002.