Abstract
Most existing algorithms for structural learning of Bayesian networks are suitable for constructing small-sized networks which consist of several tens of nodes. In this paper, we present a novel approach to the efficient and relatively-precise induction of large-scale Bayesian networks with up to several hundreds of nodes. The approach is based on the concept of Markov blanket and makes use of the divide-and-conquer principle. The proposed method has been evaluated on two benchmark datasets and a real-life DNA microarray data, demonstrating the ability to learn the large-scale Bayesian network structure efficiently.
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
Chickering, D.M.: Learning Bayesian networks is NP-complete. In: Fisher, D., Lenz, H.-J. (eds.): Learning from Data: Artificial Intelligence and Statistics V. Springer-Verlag, Berlin Heidelberg New York (1996) 121–130
Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley & Sons, New York (1991)
Friedman, N., Goldszmidt, M.: Learning Bayesian networks with local structure. In: Jordan, M.I. (ed.): Learning in Graphical Models. MIT Press, Cambridge (1999) 421–459
Friedman, N., Nachman, I., Pe’er, D.: Learning Bayesian network structure from massive datasets: the “sparse candidate” algorithm. Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI) (1999) 206–215
Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian networks to analyze expression data. Proceedings of the Fourth Annual International Conference on Computational Biology (RECOMB) (2000) 127–135
Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning 20(3) (1995) 197–243
Heckerman, D.: A tutorial on learning with Bayesian networks. In: Jordan, M.I. (ed.): Learning in Graphical Models. MIT Press, Cambridge (1999) 301–354
Margaritis, D., Thrun, S.: Bayesian network induction via local neighborhoods. Advances in Neural Information Processing Systems 12(2000) 505–511
Nikovski, D.: Constructing Bayesian networks for medical diagnosis from incomplete and partially correct statistics. IEEE Transactions on Knowledge and Data Engineering 12(4) (2000) 509–516
Pearl, J: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)
Scherf, U. et al.: A gene expression database for the molecular pharmacology of cancer. Nature Genetics 24 (2000) 236–244
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search. 2nd edn. MIT Press, Cambridge (2000)
Suzuki, J.: Learning Bayesian belief networks based on the minimum description length principle: an efficient algorithm using the B & B technique. Proceedings of the Thirteenth International Conference on Machine Learning (ICML) (1996) 462–470
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hwang, KB., Lee, J.W., Chung, SW., Zhang, BT. (2002). Construction of Large-Scale Bayesian Networks by Local to Global Search. In: Ishizuka, M., Sattar, A. (eds) PRICAI 2002: Trends in Artificial Intelligence. PRICAI 2002. Lecture Notes in Computer Science(), vol 2417. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45683-X_41
Download citation
DOI: https://doi.org/10.1007/3-540-45683-X_41
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44038-3
Online ISBN: 978-3-540-45683-4
eBook Packages: Springer Book Archive