Bayesian model averaging of naive Bayes for clustering

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 36, Issue 5, 2006, Pages 1149-1161

  Santafé, Guzmán ^a   Lozano, Jose A ^a   Larrañaga, Pedro ^a  

^a UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

Bayesian model averaging (MA); Clustering; Expectation maximization (EM); Naive Bayes

Indexed keywords

MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION; STATISTICAL METHODS;

BAYESIAN MODEL AVERAGING; CLUSTER VARIABLE; EXPECTATION-MAXIMIZATION ALGORITHM; NAIVE BAYES;

CONTROL SYSTEM ANALYSIS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; CLUSTER ANALYSIS; COMPUTER SIMULATION; EVALUATION; METHODOLOGY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CLUSTER ANALYSIS; COMPUTER SIMULATION; LIKELIHOOD FUNCTIONS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED;

EID: 33749410653     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2006.874132     Document Type: Article

References (41)

1. J. D. Banfield and A. E. Raftery, "Model-based Gaussian and non-Gaussian clustering," Biometrics, vol. 49, no. 3, pp. 803-821, Sep. 1993.
2. H. Bensmail, G. Celeux, A. E. Raftery, and C. P. Robert, "Inference in model-based cluster analysis," Statist. Comput., vol. 7, no. 1, pp. 1-10, Mar. 1997.
3. J. Pearl, Probabilistic Reasoning in Intelligent Systems. San Mateo,CA: Morgan Kaufmann, 1988.
4. F. Jensen, Bayesian Networks and Decision Graphs. New York: Springer-Verlag, 2001.
5. R. E. Neapolitan, Learning Bayesian Networks. Englewood Cliffs, NJ: Prentice-Hall, 2003.
6. P. Cheeseman and J. Stutz, "Bayesian classification (Autoclass): Theory and results," in Advances in Knowledge Discovery and Data Mining. Menlo Park, CA: AAAI Press, 1996, pp. 153-180.
7. Y. Barash and N. Friedman, "Context-specific Bayesian clustering for gene expression data," J. Comput. Biol., vol. 9, no. 2, pp. 169-191, Apr. 2002.
8. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm (with discussion)," J. R. Statist. Soc., Ser. B, vol. 39, no. 1, pp. 1-38, 1977.
9. G. McLachlan and T. Krishnan, The EM Algotithm and Extensions. New York: Wiley, 1997.
10. N. Friedman, "Learning belief networks in the presence of missing values and hidden variables," in Proc. 14th Int. Conf. Mach. Learn., 1997, pp. 125-133.
11. J. Peña, J. Lozano, and P. Larrañaga, "An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering," Pattern Recognit. Lett., vol. 21, no. 8, pp. 779-786, Jul. 2000.
12. D. Madigan and A. E. Raftery, "Model selection and accounting for model uncertainty in graphical models using Occam's window," J. Amer. Statist. Assoc., vol. 89, no. 428, pp. 1535-1546, Dec. 1994.
13. J. Hoeting, D. Madigan, A. E. Raftery, and C. Volinsky, "Bayesian model averaging," Statist. Sci., vol. 14, no. 4, pp. 382-401, 1999.
14. D. Heckerman, "A tutorial on learning with Bayesian networks," Microsoft Research, Redmond, WA, Tech. Rep. MSR-TR-95-06, 1995.
15. N. Friedman, "The Bayesian structural EM algorithm," in Proc. 14th Conf. Uncertainty Artif. Intell., 1998, pp. 129-138.
16. N. Friedman and D. Koller, "Being Bayesian about network structure: A Bayesian approach to structure discovery in Bayesian networks," Mach. Learn., vol. 50, no. 1/2, pp. 95-126, Jan./Feb. 2003.
17. J. Cerquides and R. López de Mántaras, "The indifferent naive Bayes classifier," in Proc. 16th Int. FLAIRS Conf., 2003, pp. 341-345.
18. D. Dash and G. F. Cooper, "Model averaging for prediction with discrete Bayesian networks," J. Mach. Learn. Res., vol. 5, pp. 1177-1203, 2004.
19. J. Cerquides and R. López de Mántaras, "TAN classifiers based on decomposable distributions," Mach. Learn., vol. 59, no. 3, pp. 323-354, Jun. 2005.
20. D. Dash and G. Cooper, "Exact model averaging with naive Bayesian classifiers," in Proc. 19th Int. Conf. Mach. Learn., 2002, pp. 91-98.
21. Elvira Consortium, "Elvira: An environment for probabilistic graphical models," in Proc. 1st Eur. Workshop Probab. Graph. Models, 2002, pp. 222-230.
22. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques. San Francisco, CA: Morgan Kaufmann, 2005.
23. G. F. Cooper and E. Herskovits, "A Bayesian method for the induction of probabilistic networks from data," Mach. Learn., vol. 9, no. 4, pp. 309-347, Oct. 1992.
24. B. Thiesson, "Score and information for recursive exponential models with incomplete data," in Proc. 13th Annu. Conf. Uncertainty Artif. Intell., 1997, pp. 453-463.
25. D. J. C. MacKay, "Choice of basis for Laplace approximation," Mach. Learn., vol. 33, no. 1, pp. 77-86, Oct. 1998.
26. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images," IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-6, no. 6, pp. 721-742, Nov. 1984.
27. Y. A. Shereider, Method of Statistical Testing: Monte Carlo Method. New York: Elsevier, 1964.
28. I. M. Sobol, The Monte Carlo Method. Moscow, Russia: Mir Publishers, 1984.
29. D. M. Chickering and D. Heckerman, "Efficient approximation for the marginal likelihood of Bayesian networks with hidden variables," Mach. Learn., vol. 29, no. 2/3, pp. 181-212, Nov./Dec. 1997.
30. T. M. Cover and J. A. Thomas, Elements of Information Theory. New York: Wiley, 1991.
31. J. S. Ide, F. G. Cozman, and F. Ramos, "Generating random Bayesian networks with constraints on induced width," in Proc. 16th Eur. Conf. Artif. Intell., vol. 2004, pp. 323-327.
32. P. Domingos and M. Pazzani, "On the optimality of the simple Bayesian classifier under zero-one loss," Mach. Learn., vol. 29, no. 2/3, pp. 103-130, 1997.
33. D. Hand, K. You, "Idiot's Bayes - Not so stupid after all?" Int. Statist. Rev., vol. 69, no. 3, pp. 385-398, Dec. 2001.
34. K. Y. Yeung, R. E. Bumgarner, and A. E. Raftery, "Bayesian model averaging: Development of an improved multiclass, gene selection and classification tool for microarray data," Bioinformatics, vol. 21, no. 10, pp. 2394-2402, May 2005.
35. M. Medvedovic and J. Guo, "Bayesian model-averaging in unsupervised learning from microarray data," in Proc. 4th Workshop Data Mining Bioinformatics, 2005, pp. 40-47.
36. C. Vogl, F. Sanchez-Cabo, G. Stocker, S. Hubbard, O. Wolkenhauer, and Z. Trajanoski, "A fully Bayesian model to cluster gene-expression profiles," Bioinformatics, vol. 21 (Supplement 2), pp. ii130-ii135, 2005.
37. T. R. Golub, D. K. Slonim, P. Tamayo, C. Juard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomgield, and E. S. Lander, "Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring," Science, vol. 286, no. 5439, pp. 531-537, Oct. 1999.
38. G. W. Milligan and M. C. Cooper, "An examination of procedures for determining the number of clusters in a data set," Psychometrika, vol. 58, no. 2, pp. 159-179, Jun. 1985.
39. G. Elidan and N. Friedman, "Learning the dimensionality of hidden variables," in Proc. 17th Conf. Uncertainty Artif. Intell., 2001, pp. 144-151.
40. G. Karciauskas, T. Kocka, F. V. Jensen, P. Larrañaga, and J. A. Lozano, "Learning of latent class models by splitting and merging components," in Proc. 2nd Workshop Probab. Graph. Models, 2004, pp. 137-144.
41. D. Dash and G. Cooper, "Model averaging with discrete Bayesian network classifiers," in Proc. 9th Int. Workshop Artif. Intell. Statist., 2003, pp. 38-45.