Abstract
We propose a new algorithm called SCD for learning the structure of a Bayesian network. The algorithm is a kind of constraint-based algorithm. By taking advantage of variable ordering, it only requires polynomial time conditional independence tests and learns the exact structure theoretically. A variant which adopts the Bayesian Dirichlet scoring function is also presented for practical purposes. The performance of the algorithms are analyzed in several aspects and compared with other existing algorithms. In addition, we define a new evaluation metric named EP power which measures the proportion of errors caused by previously made mistakes in the learning sequence, and use the metric for verifying the robustness of the proposed algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pearl, J.: Fusion, propagation and structuring in belief networks. In: Kanal, Lemmer (eds.) Uncertainty in Artificial Intelligence, pp. 357–370. North-Holland, Amsterdam (1986)
Spirtes, P., Glymour, C., Scheines, R., Kauffman, S., Aimale, V., Wimberly, F.: Constructing bayesian network models of gene expression networks from microarray data. In: Proceedings of the Atlantic Symposium on Computational Biology, Genome Information Systems and Technology (2001)
Cooper, G.F., Herskovits, E.: A bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347 (1992)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search. Springer, Heidelberg (1993)
Pearl, J., Verma, T.S.: A theory of inferred causation. In: Allen, J.F., Fikes, R., Sandewall, E. (eds.) KR 1991: Principles of Knowledge Representation and Reasoning, San Mateo, California, pp. 441–452. Morgan Kaufmann, San Francisco (1991)
Cheng, J., Greiner, R., Kelly, J., Bell, D.A., Liu, W.: Learning bayesian networks from data: An information-theory based approach. Artif. Intell. 137(1-2), 43–90 (2002)
Heckerman, D.: A tutorial on learning bayesian networks. Tech. Rep. MSR-95-06, Microsoft Research (1995)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc., San Mateo, California (1988)
Pearl, J.: Causality: Modeling, Reasoning, and Inference. Cambridge University Press, Cambridge (2000)
Roulston, M.S.: Estimating the errors on measured entropy and mutual information. Physica D: Nonlinear Phenomena 125, 285–294 (1999)
Ide, J.S., Cozman, F.G., Ramos, F.T.: Generating random bayesian networks with constraints on induced width. In: de Mántaras, R.L., Saitta, L. (eds.) ECAI, pp. 323–327. IOS Press, Amsterdam (2004)
Beinlich, I., Suermondt, H., Chavez, R., Cooper, G.: The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. In: Proceedings of the Second European Conference on Artificial Intelligence in Medicine, London, pp. 247–256 (1989)
Hsu, W.H.: Genetic wrappers for feature selection in decision tree induction and variable ordering in bayesian network structure learning. Inf. Sci. 163(1-3), 103–122 (2004)
Steck, H.: Constraint-Based Structural Learning in Bayesian Networks using Finite Data Sets. PhD thesis, Munich University of Technology (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, S., Yang, J., Park, S. (2006). A New Polynomial Time Algorithm for Bayesian Network Structure Learning. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_55
Download citation
DOI: https://doi.org/10.1007/11811305_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
eBook Packages: Computer ScienceComputer Science (R0)