Mutual information preconditioning improves structure learning of bayesian networks from medical databases

IEEE Transactions on Information Technology in Biomedicine

Volumn 13, Issue 6, 2009, Pages 984-989

  Meloni, Antonella ^a,b   Ripoli, Andrea ^a   Positano, Vincenzo ^a   Landini, Luigi ^a,b  

^a INSTITUTE OF CLINICAL PHYSIOLOGY   (Italy)

^b UNIVERSITY OF PISA   (Italy)

Author keywords

Bayesian network (BN); Biomedical data; Mutual information (MI); Structural learning

Indexed keywords

BAYESIAN NETWORKS (BNS); BINARIZATIONS; BINARY MATRIX; BIOMEDICAL DATA; GREEDY SEARCH; MEDICAL DATA SETS; MEDICAL DATABASE; MEDICAL DIAGNOSIS; MUTUAL INFORMATION (MI); MUTUAL INFORMATIONS; NETWORK STRUCTURES; OPTIMAL TREATMENT; SCORE ALGORITHM; SQUARE MATRICES; STRUCTURAL LEARNING; STRUCTURE-LEARNING; TREATMENT OUTCOMES;

DIAGNOSIS; DISTRIBUTED PARAMETER NETWORKS; EDUCATION; INFERENCE ENGINES; INTELLIGENT NETWORKS; LEARNING ALGORITHMS; MEDICAL COMPUTING; SPEECH RECOGNITION;

BAYESIAN NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; FACTUAL DATABASE; HUMAN; MEDICAL INFORMATICS; MEDICAL RECORD; METHODOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DATABASES, FACTUAL; HUMANS; MEDICAL INFORMATICS; MEDICAL RECORDS SYSTEMS, COMPUTERIZED;

EID: 70449567219     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2009.2026273     Document Type: Article

References (36)

1. F. V. Jensen, An Introduction to Bayesian Networks. NewYork: Springer-Verlag, 1996.
2. P. J. F. Lucas, L. C. van der Gaag, and A. Abu-Hanna, "Bayesian networks in biomedicine and health-care," Artif. Intell. Med., vol.30, pp. 201-214, 2004.
3. B. Sierra, I. Inza, and P. Larranaga, "Medical Bayesian networks," Lect. Notes Comput. Sci., vol.1933, pp. 4-14, 2000.
4. S. G. Bøttcher and C. Dethlefsen, "Deal: A package for learning Bayesian networks," J. Statist. Softw., vol.8, pp. 1-40, 2003.
5. S. G. Bøttcher, "Learning conditional Gaussian networks," Ph.D. dissertation, Dept. Math. Sci., Aalborg Univ., Aalborg, Denmark, 2005.
6. D. Heckerman, D. Geiger, and D. M. Chickering, "Learning Bayesian networks-The combination of knowledge and statistical-data," Mach. Learn., vol.20, pp. 197-243, 1995.
7. N. Friedman, D. Geiger, and M. Goldszmidt, "Bayesian network classifiers," Mach. Learn., vol.29, pp. 131-163, 1997.
8. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 2nd ed. San Mateo, CA: Morgan Kaufmann, 1988.
9. R. E. Neapolitan, Probabilistic Reasoning in Expert Systems: Theory and Algorithms. New York: Wiley, 1990.
10. P. Spirtes, C. N. Glymour, and R. Scheines, Causation, Prediction, and Search, 2nd ed., P. Spirtes, C. Glymour, and R. Scheines, Eds. Cambridge, MA: MIT Press, 2000.
11. J. Cheng, R. Greiner, J. Kelly, D. Bell, andW. R. Liu, "Learning Bayesian networks from data: An information-theory based approach," Artif. Intell., vol.137, pp. 43-90, 2002.
12. E. Herskovits, G. Cooper, and U. Stanford, "Kutat́o: An entropy-driven system for construction of probabilistic expert systems from databases," Knowl. Syst. Lab., Med. Comput. Sci., Stanford Univ., Stanford, CA, Tech. Rep. KSL-90-122, 1990.
13. R. R. Bouckaert, "Probabilistic network construction using the minimum description length principle," Lect. Notes Comput. Sci., vol.747, pp. 41- 41, 1993.
14. G. F. Cooper and E. Herskovits, "A Bayesian method for the induction of probabilistic betworks from data," Mach. Learn., vol.9, pp. 309-347, 1992.
15. D. C. Chickering, D. Geiger, and D. Heckerman, "Learning Bayesian networks: Search methods and experimental results," in Proc. 5th Conf. Artif. Intell. Stat., 1995, pp. 112-128.
16. R. R. Bouckaert, "Bayesian belief networks: From construction to inference," Ph.D. dissertation, Utrecht Univ., Utrecht, The Netherlands, 1995.
17. J. Tian, "A branch-and-bound algorithm for MDL learning Bayesian networks," in Proc. 16th Conf. Uncertainty Artif. Intell., 2000, pp. 580-588.
18. P. Larranaga, M. Poza, Y. Yurramendi, R. H. Murga, and C. M. H. Kuijpers, "Structure learning of Bayesian networks by genetic algorithms: Aperformance analysis of control parameters," IEEE Trans. Pattern Anal. Mach. Intell., vol.18, no.9, pp. 912-926, Sep. 1996.
19. L. M. de Campos and J. M. Puerta, "Stochastic local and distributed search algorithms for learning belief networks," in Proc. 3rd Int. Symp. Probabilistic Graph. Models, 2001, pp. 109-115.
20. J.W. Myers, K. B. Laskey, and T. S. Levitt, "Learning Bayesian networks from incomplete data with stochastic search algorithms," in Proc. 15th Annu. Conf. Uncertainty Artif. Intell., 1999, pp. 476-485.
21. R. Blanco, I. Inza, and P. Larranga, "Learning Bayesian networks in the space of structures by estimation of distribution algorithms," Int. J. Intell. Syst., vol.18, pp. 205-220, 2003.
22. T. Kocka and R. Castelo, "Improved learning of Bayesian networks," in Proc. 17th Conf. Uncertainty Artif. Intell., 2001, pp. 269-276.
23. L. M. de Campos, J. M. Fernandez-Luna, J. A. Gamez, and J. M. Puerta, "Ant colony optimization for learning Bayesian networks," Int. J. Approx. Reason., vol.31, pp. 291-311, 2002.
24. M. Singh and M. Valtorta, "Construction of Bayesian network structures from data: A brief survey and an efficient algorithm," Int. J. Approx. Reason., vol.12, pp. 111-131, 1995.
25. D. Dash and M. Druzdzel, "A hybrid anytime algorithm for the construction of causal models from sparse data," in Proc. 15th Int. Conf. Uncertainty Artif. Intell., 1999, pp. 142-149.
26. S. Acid and L. M. de Campos, "A hybrid methodology for learning belief networks: BENEDICT," Int. J. Approx. Reason., vol.27, pp. 235-262, 2001. (Pubitemid 32610603)
27. T. M. Cover and J. A. Thomas, Elements of Information Theory. New York: Interscience, 2006.
28. C. E. Shannon and W. Weaver, "A mathematical theory of communications," Bell Syst. Tech. J., vol.27, pp. 632-656, 1948.
29. A. Kolmogorov, "Logical basis for information theory and probability theory," IEEE Trans. Inf. Theory, vol.IT-14, no.5, pp. 662-664, Sep. 1968.
30. F. Bazso, L. Zalanyi, and A. Petroczi, "Accuracy of joint entropy and mutual information estimates," in Proc. IEEE Int. Joint Conf. Neural Netw., Jul. 2004, vol.4, pp. 2843-2846.
31. G. D. Tourassi, E. D. Frederick,M. K.Markey, and C. E. Floyd, "Application of the mutual information criterion for feature selection in computeraided diagnosis," Med. Phys., vol.28, pp. 2394-2402, 2001.
32. C. Ding and H. Peng, "Minimum redundancy feature selection from microarray gene expression data," J. Bioinf. Comput. Biol., vol.3, pp. 185- 205, 2005.
33. R. J. Hyndman and Y. N. Fan, "Sample quantiles in statistical packages," Amer. Stat., vol.50, pp. 361-365, 1996.
34. S. L. Lauritzen and D. J. Spiegelhalter, "Local computations with probabilities on graphical structures and their application to expert systems," J. R. Stat. Soc., vol.50, pp. 157-224, 1988.
35. M. Henrion, "Propagating uncertainty by logic sampling in Bayesian networks," in Proc.Uncertainty Artif. Intell. Conf., Elsevier, NewYork, 1988, pp. 149-163.
36. P. E. Meyer, K. Kontos, F. Lafitte, and G. Bontempi, " Informationtheoretic inference of large transcriptional regulatory networks," EURASIP J. Bioinf. Syst. Biol., vol.2007, no.1, pp. 9, 2007.