Learning Bayesian network classifiers: Searching in a space of partially directed acyclic graphs

Machine Learning

Volumn 59, Issue 3, 2005, Pages 213-235

  Acid, Silvia ^a   De Campos, Luis M ^a   Castellano, Javier G ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

Bayesian networks; Classification; Directed acyclic graphs; Learning algorithms; Partially directed acyclic graphs; Scoring functions

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; EQUIVALENCE CLASSES; FUNCTIONS; GRAPHIC METHODS; LEARNING ALGORITHMS; ONLINE SEARCHING;

BAYESIAN NETWORKS; DIRECTED ACYCLIC GRAPHS; PARTIALLY DIRECTED ACYCLIC GRAPHS; SCORING FUNCTIONS;

CLASSIFICATION (OF INFORMATION);

EID: 21244467165     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-005-0473-4     Document Type: Article

References (39)

1. Acid, S., & de Campos, L. M. (2001). A hybrid methodology for learning belief networks: Benedict. International Journal of Approximate Reasoning, 27, 235-262.
2. Acid, S., & de Campos, L. M. (2003). Searching for Bayesian network structures in the space of restricted acyclic partially directed graphs. Journal of Artificial Intelligence Research, 18, 445-490.
3. Aliferis, C. F., Tsamardinos, I., & Statnikov, A. (2003). HITON: A novel Markov blanket algorithm for optimal variable selection. In Proceedings of the 2003 American Medical Informatics Association (pp. 21-25).
4. Blake, C. L., & Merz, C. J. (1998). UCI Repository of machine learning databases, http://www.ics.uci.edu/~mlearn/MLRepository.html, University of California, Irvine, Dept. of Information and Computer Sciences.
5. Cheng, J., & Greiner, R. (1999). Comparing Bayesian network classifiers. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (pp. 101-108).
6. Cheng, J., Greiner, R., Kelly, J., Bell, D. A., & Liu, W. (2002). Learning Bayesian networks from data: An information-theory based approach. Artificial Intelligence, 137, 43-90.
7. Chickering, D. M. (1996). Learning equivalence classes of Bayesian network structures. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (pp. 150-157).
8. Chickering, D. M. (2002). Learning equivalence classes of Bayesian network structures. Journal of Machine Learning Research, 2, 445-498.
9. Chow, C., & Liu, C. (1968). Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, 14, 462-467.
10. Cooper, G. F., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309-348.
11. de Campos, L. M., & Huete, J.F. (2000). A new approach for learning belief networks using independence criteria. International Journal of Approximate Reasoning, 24, 11-37.
12. Domingos, P., & Pazzani, M. J. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29, 103-130.
13. Elvira Consortium. (2002). Elvira: An environment for probabilistic graphical models. In Proceedings of the First European Workshop on Probabilistic Graphical Models (pp. 222-230).
14. Ezawa, K., Singh, M., & Norton, S. (1996). Learning goal oriented Bayesian networks for telecommunications risk management. In Proceedings of the Thirteenth International Conference on Machine Learning (pp. 139-147).
15. Fayyad, U. M., & Irani, K. B. (1993). Multi-valued interval discretization of continuous-valued attributes for classification learning. In Proceedings of the Thirteenth International Joint Conference on Artificial Inteligence (pp. 1022-1027).
16. Frey, L., Fisher, D., Tsamardinos, I., Aliferis, C. F., & Statnikov, A. (2003). Identifying Markov blankets with decision tree induction. In Proceedings of the Third IEEE International Conference on Data Mining (pp. 59-66).
17. Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29, 131-163.
18. Good, I. J. (1965). The estimation of probabilities. Cambridge: The MIT Press.
19. Hand, D. J. (1981). Discrimination and classification. Wiley.
20. Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20, 197-243.
21. John, G. H., & Kohavi, R. (1994). Irrelevant features and the subset selection problem. In Proceedings of the Eleventh International Conference on Machine Learning (pp. 121-129).
22. Keogh, E., & Pazzani, M. (2002). Learning augmented Bayesian classifiers. International Journal on Artificial Intelligence Tools, 11, 587-601.
23. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (pp. 1137-1143).
24. Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97, 273-324.
25. Kohavi, R., John, G. H., Long, R., Manley, D., & Pfleger, K. (1994). MLC++: A machine learning library in C++. In Proceedings of the Sixth International Conference on Tools with Artificial Intelligence (pp. 740-743).
26. Koller, D., & Sahami, M. (1996). Toward optimal feature selection. In Proceedings of the Thirteenth International Conference on Machine Learning (pp. 284-292).
27. Kononenko, I. (1991). Semi-naive Bayesian classifier. In Proceedings of the Second International Conference on Knowledge Discovery in Databases (pp. 206-219).
28. Lam, W., & Bacchus, F. (1994). Learning Bayesian belief networks. An approach based on the MDL principle. Computational Intelligence, 10, 269-293.
29. Langley, P., Iba, W., & Thompson, K. (1992). An analysis of Bayesian classifiers. In Proceedings of the National Conference on Artificial Intelligence (pp. 223-228).
30. Langley, P., & Sage, S. (1994). Induction of selective Bayesian classifiers. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (pp. 399-406).
31. Lucas, P. (2002). Restricted Bayesian network structure learning. In Proceedings of the First European Workshop on Probabilistic Graphical Models (pp. 117-126).
32. Pazzani, M. J. (1995). Searching for dependencies in Bayesian classifiers. Lecture Notes in Statistics, 112, 239-248.
33. Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Francisco, CA: Morgan Kaufmann.
34. Pearl, J., & Verma, T.S. (1990). Equivalence and synthesis of causal models. In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (pp. 220-227).
35. Sahami, M. (1996). Learning limited dependence Bayesian classifiers. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (pp. 335-338).
36. Sierra, B. & Larrañaga, P. (1998). Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches. Artificial Intelligence in Medicine, 14, 215-230.
37. Singh, M., & Provan, G. M. (1996). Efficient learning of selective Bayesian network classifiers. In Proceedings of the Thirteenth International Conference on Machine Learning (pp. 453-461).
38. Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, Prediction and Search. Lecture Notes in Statistics 81. New York: Springer Verlag.
39. Zhang, N. L., Nielsen, T. D., & Jensen, F. V. (2004). Latent variable discovery in classification models. Artificial Intelligence in Medicine, 30, 283-299.