Abstract
Various different algorithms for learning Bayesian networks from data have been proposed to date. In this paper, we adopt a novel approach that combines the main advantages of these algorithms yet avoids their difficulties. In our approach, first an undirected graph, termed the skeleton, is constructed from the data, using zero- and first-order dependence tests. Then, a search algorithm is employed that builds upon a quality measure to find the best network from the search space that is defined by the skeleton. To corroborate the feasibility of our approach, we present the experimental results that we obtained on various different datasets generated from real-world networks. Within the experimental setting, we further study the reduction of the search space that is achieved by the skeleton.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Buntine, W.L.: Classifiers: A theoretical and empirical study. In: de Mantaras, R.L., Poole, D. (eds.) Proceedings of the International Joint Conference on Artificial Intelligence, pp. 638–644. 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. Artificial Intelligence 137(1–2), 43–90 (2002)
Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347 (1992)
Cowell, R.G., Dawid, A.P., Lauritzen, S.L., Spiegelhalter, D.J.: Probabilistic Networks and Expert Systems. Statistics for Engineering and Information Science. Springer, Heidelberg (1999)
de Campos, L.M., Huete, J.F.: A new approach for learning belief networks using independence criteria. International Journal of Approximate Reasoning 24(1), 11–37 (2000)
Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 20(3), 197–243 (1995)
Lam, W., Bacchus, F.: Using causal information and local measures to learn Bayesian networks. In: Heckerman, D., Mamdani, A. (eds.) Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, pp. 243–250. Morgan-Kaufmann, San Francisco (1993)
Rebane, G., Pearl, J.: The recovery of causal poly-trees from statistical data. In: Kanal, L.N., et al. (eds.) Proceedings of the Third Conference on Uncertainty in Artificial Intelligence, pp. 175–182. Elsevier, Amsterdam (1987)
Spirtes, P., Glymour, C.: An algorithm for fast recovery of sparse causal graphs. Social Science Computer Review 9(1), 62–73 (1991)
Steck, H., Tresp, V.: Bayesian belief networks for data mining. In: Proceedings of the Second Workshop on Design and Management of Data Warehouses, pp. 145–154 (1999)
van Dijk, S., Thierens, D., van der Gaag, L.C.: Building a GA from design principles for learning Bayesian networks. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 886–897. Springer, Heidelberg (2003)
Wong, M.L., Lee, S.Y., Leung, K.S.: A hybrid data mining approach to discover Bayesian networks using evolutionary programming. In: Langdon, W.B., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, Morgan-Kaufmann, San Francisco (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
van Dijk, S., van der Gaag, L.C., Thierens, D. (2003). A Skeleton-Based Approach to Learning Bayesian Networks from Data. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds) Knowledge Discovery in Databases: PKDD 2003. PKDD 2003. Lecture Notes in Computer Science(), vol 2838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39804-2_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-39804-2_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20085-7
Online ISBN: 978-3-540-39804-2
eBook Packages: Springer Book Archive