Towards efficient variables ordering for Bayesian networks classifier

Data and Knowledge Engineering

Volumn 63, Issue 2, 2007, Pages 258-269

  Hruschka Jr , Estevam R ^a   Ebecken, Nelson F F ^b  

^a FEDERAL UNIVERSITY OF SÃO CARLOS   (Brazil)

^b FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

Author keywords

Bayesian networks classifiers; Feature ranking; Supervised learning; Variable ordering

Indexed keywords

BAYESIAN NETWORKS CLASSIFIERS; BAYESIAN STRUCTURES; FEATURE RANKING; VARIABLE ORDERING;

ALGORITHMS; APPROXIMATION THEORY; COMPUTATIONAL COMPLEXITY; LEARNING SYSTEMS; PROBLEM SOLVING; STATE SPACE METHODS;

BAYESIAN NETWORKS;

EID: 34447281088     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2007.02.003     Document Type: Article

References (55)

1. Acid S., de Campos L.M., and Huete J.F. The search of causal orderings: a short cut for learning belief networks. Proceedings of the Sixth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty. Lecture Notes in Computer Science vol. 2142 (2001), Springer, Berlin 216-227
2. Acid S., and De Campos L.M. Searching for Bayesian Network structures in the space of restricted acyclic partially directed graphs. Journal of Artificial Intelligence Research 18 (2003) 445-490
3. Acid S., and de Campos L.M. A hybrid methodology for learning belief networks: BENEDICT. International Journal of Approximate Reasoning 27 (2001) 235-262
4. S. Acid, L.M. de Campos, Benedict: an algorithm for learning probabilistic belief networks, in: Proceedings of the IPMU-96 Conference, 1996, pp. 979-974.
5. Aha D.W., Kibler D., and Albert M.K. Instance-based learning algorithms. Machine Learning 6 (1991) 37-66
6. Blum A.L., and Langley P. Selection of relevant features and examples in machine learning. Artificial Intelligence (1997) 245-271
7. R.R. Bouckaert, Optimizing causal orderings for generating DAGs from data, in: Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence, 1992, pp. 9-16.
8. R.R. Bouckaert, Bayesian Networks in Weka. Technical Report 14/2004. Computer Science Department. University of Waikato, 2004.
9. Campos L.M., and Huete J.F. A new Approach for learning belief networks using independence criteria International. Journal of Approximate Reasoning 24 (2000) 11-37
10. Campos L.M., Fernadez-Luna J.M., and Puerta J.M. Local search methods for learning bayesian networks using a modified neighborhood in the space of DAGs. Proceedings of the Eighth Ibero-American Conference on AI. Lecture Notes in Artificial Intelligence vol. 2527 (2002), Springer, Berlin 182-192
11. Cano R., Sordo C., and Gutiérrez J.M. Applications of Bayesian Networks in Meteorology, Advances in Bayesian Networks. In: Gámez J.A., et al. (Ed) (2004), Springer-Verlag 309-327
12. Chickering D.M. Optimal Structure Identification with Greedy Search. Journal of Machine Learning Research 3 (2002) 507-554
13. Chickering D.M., Meek C., and Heckerman D. Large-sample learning of Bayesian networks is NP-hard. Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence (2003), Morgan-Kaufmann 124-133
14. Cooper G., and Herskovitz E. A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9 (1992) 309-347
15. R.G. Cowell, Conditions under which conditional independence and scoring methods lead to identical selection of Bayesian Network Models, in: Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence, 2001, pp. 91-97.
16. D. Dash, M. Druzdzel, A hybrid anytime algorithm for the construction of causal models from sparse data, in: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 1999, pp. 142-149.
17. L.M. de Campos, J.F. Huete, Approximating Causal Orderings for Bayesian Networks using Genetic Algorithms and Simulated Annealing, Technical Report DECSAI-990212, Research Group Uncertainty Treatment in Artificial Intelligence, UTAI of the University of Granada, 1999.
18. de Campos M. Independency relationships and learning algorithms for singly connected networks. Journal of Experimental and Theoretical Artificial Intelligence 10 (1998) 511-549
19. DeGroot M.H. Optimal Statistical Decision (1970), McGraw-Hill
20. Di Zio M., Scanu M., Coppola L., Luzi O., and Ponti A. Bayesian networks for imputation. Journal of the Royal Statistical Society A 167 Part 2 (2004) 309-322
21. Duda R.O., and Hart P.E. Pattern Classification and Scene Analysis (1973), John Wiley & Sons, New York
22. Friedman N., and Koller D. Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks. Machine Learning 50 (2003) 95-125
23. Friedman N., Geiger D., and Goldszmidt M. Bayesian Network classifiers. Machine Learning 29 (1997) 131-163
24. Friedman N., Linial M., Nachman I., and Peter D. Using Bayesian networks to analyze expression data. Proceedings of the Fourth International Annual Conference on Computational Molecular Biology (2000), ACM Press 127-135
25. Gelman A., Carlin J.B., Stern H.S., and Rubin D.B. Bayesian Data Analysis (1995), Chapman and Hall, London
26. Giudici P., and Green P. Decomposable graphical Gaussian model determination. Biometrika 86 4 (1999) 785-801
27. P. Giudici, P. Green, C. Tarantola, Efficient Model Determination for Discrete Graphical Models.Discussion Paper 99-93, Department of Statistics, Athens University of Economics and Business, 2000.
28. Glover F. Future paths for Integer Programming and Links to Artificial Intelligence. Computers and Operations Research 5 (1986) 533-549
29. D. Grossman, P. Domingos, Learning Bayesian networks classifiers by maximizing conditional likelihood. in: Proceedings of the 21st ICML, 2004.
30. H. Guo, B.B. Perry, J.A. Stilson, W.H. Hsu, A genetic algorithm for tuning variable orderings in Bayesian Network structure learning, in: Proceedings of the Eighteenth National Conference on Artificial Intelligence, 2002, pp. 951-952.
31. Guyon I., and Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Research 3 (2003) 1157-1182
32. E.R. Hruschka Jr., N.F.F. Ebecken, Variable ordering for Bayesian Networks learning from data, in: Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation, Vienna, 2003.
33. Hsu W.H. Genetic Wrappers for feature selection in decision tree induction and variable ordering in Bayesian network structure learning. Information Science 163 (2004) 103-122
34. Kaufman L., and Rousseeuw P.J. Finding Groups in Data - An Introduction to Cluster Analysis Wiley Series in Probability and Mathematical Statistics (1990), John Wiley & Sons Inc.
35. D. Koller, M. Sahami, Toward optimal feature selection, in: Proceedings of the 13th International Conference on Machine Learning, 1996, pp. 284-292.
36. Lam W., and Bacchus F. Learning Bayesian Belief Networks, an approach based on the MDL principle. Computational Intelligence 10 (1994) 269-293
37. Langley P., Iba W., and Thompson K. An analysis of Bayesian Classifiers. Proceedings of th 10th National Conference on Artificial Intelligence (1992), AAAI-Press 223-228
38. Larrañaga P., Kuijpers C.M., Murga R., and Yurramendi Y. Learning Bayesian network structures by searching for best ordering with genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics 26 4 (1996) 487-493
39. Liu H., and Motoda H. Feature Selection for Knowledge Discovery and Data Mining (1998), Kluwer Academic
40. Madigan D., and Raftery E. Model selection and accounting for model uncertainty in graphical models using Occam's window. Journal American Statistical Association 89 (1994) 1535-1546
41. Madigan D., and York J. Bayesian graphical models for discrete data. International Statistical Review 63 (1995) 215-232
42. Madigan D., Andersson S., Perlman M., and Volinsky C. Bayesian model averaging and model selection for Markov equivalence classes of acyclic graphs. Communications in Statistics: Theory and Methods 25 (1996) 2493-2519
43. C.J. Merz, P.M. Murphy, UCI Repository of Machine Learning Databases, Irvine, CA, University of California, Department of Information and Computer Science. .
44. Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (1988), Morgan Kaufmann, San Mateo, CA
45. Provost F., and Fawcett T. Robust classification for imprecise environments. Machine Learning 42 (2001) 203-231
46. Quinlan J.R. Induction of decision trees. Machine Learning 1 (1986) 81-106
47. J.R. Quinlan, Unknown attribute values in induction, in: Proceedings of Sixth International Workshop on Machine Learning, Ithaca, NY, 1989, pp. 164-168.
48. Reunanen J. Overfitting in making comparisons between variable selection methods. Journal of Machine Learning Research 3 (2003) 1371-1382
49. Singh S., and Valtorta M. Construction of Bayesian Network structures from data: a brief survey and an efficient algorithm international. Journal of Approximate Reasoning 12 2 (1995) 111-131
50. Spiegelhalter D.J., and Lauritzen S.L. Sequential updating of conditional probability on direct graphical structures. Networks 20 (1990) 576-606
51. Spirtes P., Glymour C., and Scheines R. Causation, Predication, and Search (1993), Springer-Verlag, New York
52. Spirtes P., and Glymour C. An algorithm for fast recovery of sparse causal graphs. Social Science Computer Review 9 (1991) 62-72
53. T. Verma, J. Pearl, An algorithm for deciding if a set of observed independencies has a causal explanation, in: Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence, 1992, pp. 323-330.
54. T. Verma, J. Pearl, Equivalance and synthesis of causal models, in: Proceedings of the Sixth Conference in Artificial Intelligence, 1991, pp. 220-227.
55. Witten I.H., and Frank E. Data Mining - Practical Machine Learning Tools and Techniques with Java Implementations (2000), Morgan Kaufmann Publishers, USA