Abstract
In this paper we explore the use of several types of structural restrictions within algorithms for learning Bayesian networks. These restrictions may codify expert knowledge in a given domain, in such a way that a Bayesian network representing this domain should satisfy them. Our objective is to study whether the algorithms for automatically learning Bayesian networks from data can benefit from this prior knowledge to get better results. We formally define three types of restrictions: existence of arcs and/or edges, absence of arcs and/or edges, and ordering restrictions, and also study their interactions and how they can be managed within Bayesian network learning algorithms based on the score+search paradigm. Then we particularize our study to the classical local search algorithm with the operators of arc addition, arc removal and arc reversal, and carry out experiments using this algorithm on several data sets.
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References
Abramson, B., Brown, J., Murphy, A., Winkler, R.L.: Hailfinder: A Bayesian system for forecasting severe weather. International Journal of Forecasting 12, 57–71 (1996)
Acid, S., de Campos, L.M.: Searching for Bayesian network structures in the space of restricted acyclic partially directed graphs. Journal of Artificial Intelligence Research 18, 445–490 (2003)
Beinlich, I.A., Suermondt, H.J., Chavez, R.M., Cooper, G.F.: The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks. In: Proceedings of the European Conference on Artificial Intelligence in Medicine, pp. 247–256 (1989)
Binder, J., Koller, D., Russell, S., Kanazawa, K.: Adaptive probabilistic networks with hidden variables. Machine Learning 29, 213–244 (1997)
Cheng, J., Greiner, R.: Comparing Bayesian network classifiers. In: Proceedings of the Fifteenth UAI Conference, pp. 101–108 (1999)
Chickering, D.M.: A transformational characterization of equivalent Bayesian network structures. In: Proceedings of the Eleventh UAI Conference, pp. 87–98 (1995)
Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–348 (1992)
de Campos, L.M., Fernández-Luna, J.M., Gámez, J.A., Puerta, J.M.: Ant colony optimization for learning Bayesian networks. International Journal of Approximate Reasoning 31, 291–311 (2002)
de Campos, L.M., Fernández-Luna, J.M., Puerta, J.M.: Local search methods for learning Bayesian networks using a modified neighborhood in the space of dags. In: Garijo, F.J., Riquelme, J.-C., Toro, M. (eds.) IBERAMIA 2002. LNCS (LNAI), vol. 2527, pp. 182–192. Springer, Heidelberg (2002)
de Campos, L.M., Puerta, J.M.: Stochastic local and distributed search algorithms for learning belief networks. In: Proceedings of the III International Symposium on Adaptive Systems: Evolutionary Computation and Probabilistic Graphical Model, pp. 109–115 (2001)
De Campos, L.M., Puerta, J.M.: Stochastic local algorithms for learning belief networks: Searching in the space of the orderings. In: Benferhat, S., Besnard, P. (eds.) ECSQARU 2001. LNCS (LNAI), vol. 2143, pp. 228–239. Springer, Heidelberg (2001)
Consortium, E.: Elvira: an environment for probabilistic graphical models. In: Gámez, J.A., Salmerón, A. (eds.) Proceedings of the 1st European Workshop on Probabilistic Graphical Models, pp. 222–230 (2002)
Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 20, 197–243 (1995)
Langley, P., Sage, S.: Induction of selective Bayesian classifiers. In: Proceedings of the Tenth UAI Conference, pp. 399–406 (1994)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction and Search. Lecture Notes in Statistics, vol. 81. Springer, New York (1993)
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de Campos, L.M., Castellano, J.G. (2005). On the Use of Restrictions for Learning Bayesian Networks. In: Godo, L. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2005. Lecture Notes in Computer Science(), vol 3571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11518655_16
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DOI: https://doi.org/10.1007/11518655_16
Publisher Name: Springer, Berlin, Heidelberg
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