Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm

Decision Support Systems

Volumn 45, Issue 2, 2008, Pages 368-383

  Wong, Man Leung ^a   Guo, Yuan Yuan ^a  

^a LINGNAN UNIVERSITY   (Hong Kong)

Author keywords

Bayesian networks; Data mining; Evolutionary algorithms; Machine learning

Indexed keywords

BAYESIAN NETWORKS; DATA MINING; DATABASE SYSTEMS; EVOLUTIONARY ALGORITHMS; LEARNING ALGORITHMS;

EXPECTATION MAXIMIZATION (EM) ALGORITHM; SUB-OPTIMAL SOLUTIONS;

LEARNING SYSTEMS;

EID: 43249121828     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2008.01.002     Document Type: Article

References (54)

1. Ahn J.-H., and Ezawa K.J. Decision support for real-time telemarketing operations through Bayesian network learning. Decision Support Systems 21 1 (September 1997) 17-27
2. Andreassen S., Woldbye M., Falck B., and Andersen S. MUNIN: a causal probabilistic network for interpretation of electromyographic findings. Proceedings of the Tenth International Joint Conference on Artificial Intelligence (1987) 366-372
3. Beaumont G.P., and Knowles J.D. Statistical Tests: An Introduction with MINITAB Commentary (1996), Prentice-Hall
4. Beinlinch I., Suermondt H., Chavez R., and Cooper G. The ALARM monitoring system: a case study with two probabilistic inference techniques for belief networks. Proceedings of Second European Conference Artificial Intelligence in Medicine (1989) 247-256
5. Bhattacharyya S. Direct marketing response models using genetic algorithms. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (1998) 144-148
6. Blanco R., Inza I., and Larrañaga P. Learning Bayesian networks in the space of structures by estimation of distribution algorithms. International journal of Intelligent Systems 18 2 (2003) 205-220
7. Cai Z., Heydari M., and Lin G. Microarray missing value imputation by iterated local least squares. Proceedings of the Fourth Asia-Pacific Bioinformatics Conference (2006) 159-168
8. Cheng J., Greiner R., Kelly J., Bell D., and Liu W. Learning Bayesian networks from data: an information-theory based approach. Artificial Intelligence 137 (2002) 43-90
9. Chickering D.M. Learning equivalence classes of Bayesian network structures. Journal of Machine Learning Research 2 (2002) 445-498
10. Cooper G., and Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9 4 (1992) 309-347
11. Cotta C., and Muruzábal J. Towards a more efficient evolutionary induction of Bayesian networks. Proceedings of The Seventh International Conference on Parallel Problem Solving From Nature (2002), Springer-Verlag 730-739
12. Cotta C., and Muruzábal J. On the learning of Bayesian network graph structures via evolutionary programming. Proceedings of the Second European Workshop on Probabilistic Graphical Models (2004) 65-72
13. de Campos L.M., Fernandez-Luna J.M., Gamez J.A., and Puerta J.M. Ant colony optimization for learning Bayesian networks. International Journal of Approximate Reasoning 31 3 (November 2002) 291-311
14. Dempster A.P., Laird N.M., and Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society (B) 39 1 (1977) 1-38
15. Djan-Sampson P.O., and Sahin F. Structural learning of Bayesian networks from complete data using the scatter search documents. Proceedings of IEEE International Conference on Systems, Man and Cybernetics (2004), IEEE Press 3619-3624
16. Friedman N. Learning belief networks in the presence of missing values and hidden variables. Proceedings of the Fourteenth International Conference on Machine Learning (1997), Morgan Kaufmann 125-133
17. Friedman N. The Bayesian structural EM algorithm. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (1998) 129-138
18. Friedman N., Geiger D., and Goldszmidt M. Bayesian network classifiers. Machine Learning 29 (1997) 131-163
19. Geman S., and Geman D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6 (1984) 721-742
20. D. Heckerman. A tutorial on learning Bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research Adv. Technol. Div., Redmond, WA, 1995.
21. Heckerman D., and Horvitz E. Inferring informational goals from free-text queries: a Bayesian approach. In: Cooper G.F., and Moral S. (Eds). Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (July 1998), Morgan Kaufmann, Wisconsin 230-237
22. Heckerman D., and Wellman M.P. Bayesian networks. Communications of the ACM 38 3 (March 1995) 27-30
23. Hruschka E.R.J., and Ebecken N.F.F. Missing values prediction with K2. Intelligent Data Analysis 6 6 (2002) 557-566
24. Huang C., and Darwiche A. Inference in belief networks: a procedural guide. International Journal of Approximate Reasoning 15 3 (1996) 225-263
25. Huang Z., Li J., Su H., Watts G.S., and Chen H. Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining. Decision Support Systems 43 4 (August 2007) 1207-1225
26. Jensen F.V. An Introduction to Bayesian Network (1996), University of College London Press
27. Kim S.-H. Stochastic ordering and robustness in classification from a Bayesian network. Decision Support Systems 39 3 (May 2005) 253-266
28. Kim H., Golub G.H., and Park H. Imputation of missing values in DNA microarray gene expression data. Proceedings of IEEE Computational Systems Bioinformatics Conference (2004), IEEE Press 572-573
29. Lakshminarayan K., Harp S.A., Goldman R., and Samad T. Imputation of missing data using machine learning techniques. Proceeding of the Second International Conference on Knowledge Discovery and Data Mining (1996) 140-146
30. Lam W., and Bacchus F. Learning Bayesian belief networks: an approach based on the MDL principle. Computational Intelligence 10 (1994) 269-293
31. Larrañaga P., Kuijpers C., Mura R., and Yurramendi Y. Learning Bayesian network structures by searching for the best ordering with genetic algorithms. IEEE Transactions on System, Man and Cybernetics 26 4 (1996) 487-493
32. Larrañaga P., Poza M., Yurramendi Y., Murga R., and Kuijpers C. Structure learning of Bayesian network by genetic algorithms: a performance analysis of control parameters. IEEE transactions on Pattern Analysis and Machine Intelligence 18 9 (1996) 912-926
33. Lauria E.J.M., and Duchessi P.J. A Bayesian belief network for IT implementation decision support. Decision Support Systems 42 3 (December 2006) 1573-1588
34. Lauritzen S.L. The EM algorithm for graphical association models with missing data, Computational Statistics and Data Analysis 19 (1995) 191-201
35. Lauritzen S.L., and Spiegelhalter D.J. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society (B) 50 2 (1988) 157-224
36. Little R.J.A., and Rubin D.B. Statistical Analysis with Missing Data (2002), Wiley
37. Liu L., Hawkins D.M., Ghosh S., and Young S. Robust singular value decomposition analysis of microarray data. Proceedings of the National Academy of Sciences of the United States of America 100 23 (2003) 13167-13172
38. Myers J.W., Laskey K.B., and DeJong K.A. Learning Bayesian networks from incomplete data using evolutionary algorithms. Proceedings of the Fourth Annual Conference on Genetic and Evolutionary Computation Conference 1999 (July 1999), Morgan Kauff{ligature}man, Orlando, Florida 458-465
39. Neal R.M. Bayesian Learning for Neural Networks (1996), Springer-Verlag
40. Peña J.M., Lozano J.A., and Larrañaga P. An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering. Pattern Recognition Letters 21 (2000) 779-786
41. Peña J.M., Lozano J.A., and Larrañaga P. Learning recursive Bayesian multinets for data clustering by means of constructive induction. Machine Learning 47 (2002) 63-89
42. Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (1988), Morgan Kaufmann, San Mateo, CA
43. Ramoni M., and Sebastiani P. Efficient parameter learning in Bayesian networks from incomplete databases. Technical Report KMI-TR-41 (1997), Knowledge Median Institute, The Open University
44. Ramoni M., and Sebastiani P. The use of exogenous knowledge to learn Bayesian networks from incomplete databases. Technical Report KMI-TR-44 (1997), Knowledge Median Institute, The Open University
45. Ramoni M., and Sebastiani P. Robust learning with missing data. Machine Learning 45 (2001) 147-170
46. Rubin D.B. Multiple Imputation for Nonresponse in Surveys (1987), Wiley
47. Rud O.P. Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management (2001), Wiley, New York
48. Schafer J.L., and Graham J.W. Missing data: our view of the state of the art. Psychological Methods 7 2 (2002) 147-177
49. Spirtes P., Glymour C., and Scheines R. Causation, Prediction, and Search. second ed. (2000), MIT Press
50. Wong M.L., and Leung K.S. An efficient data mining method for learning Bayesian networks using an evolutionary algorithm-based hybrid approach. IEEE Transactions on Evolutionary Computation 8 4 (August 2004) 378-404
51. Wong M.L., Lam W., and Leung K.S. Using evolutionary programming and minimum description length principle for data mining of Bayesian networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 21 (February 1999) 174-178
52. Wong M.L., Lee S.Y., and Leung K.S. Data mining of Bayesian networks using cooperative coevolution. Decision Support Systems 38 3 (December 2004) 451-472
53. Zahavi J., and Levin N. Issues and problems in applying neural computing to target marketing. Journal of Direct Marketing 11 4 (1997) 63-75
54. Zio M.D., Scanu M., Coppola L., Luzi O., and Ponti A. Bayesian networks for imputation. Journal of the Royal Statistical Society (A) 167 2 (2004) 309-322