Abstract
The implicit knowledge in the databases can be extracted of automatic form. One of the several approaches considered for this problem is the construction of graphical models that represent the relations between the variables and regularities in the data. In this work the problem is addressed by means of an algorithm of search and scoring. These kind of algorithms use a heuristic mechanism search and a function of score to guide themselves towards the best possible solution.
The algorithm, which is implemented in the semifunctional language Lisp, is a searching mechanism of the structure of a bayesian network (BN) based on concurrent processes.
Each process is assigned to a node of the BN and effects one of three possible operations between its node and some of the rest: to put, to take away or to invert an edge. The structure is constructed using the metric MDL (made up of three terms), whose calculation is made of distributed way, in this form the search is guided by selecting those operations between the nodes that minimize the MDL of the network.
In this work are presented some results of the algorithm in terms of comparison of the structure of the obtained network with respect to its gold network.
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
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 (2003)
Ben-Ari, M.: Principles of Concurrent and Distributed Programming, Great Britain. Hoare, C.A.R. (series ed.) (1990)
Castillo, E.M., Gutiérrez, J.M., Hadi, A.S.: Sistemas Expertos y Modelos de Redes Probabilísticas. Academia Española de Ingeniería (1998), http://personales.unican.es/gutierjm/BookCGH.html
Chen, R., Sivakumar, K., Kargupta, H.: Collective mining of bayesian networks from distributed heterogeneous data. In: Knowledge and Information Systems (2004)
Kohavi, R.: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: International Joint Conference on Artificial Intelligence (IJCAI), USA (1995)
Cruz-Ramírez, N.: Building Bayesian Networks from Data: a Constraint-based Approach. Ph.D. Thesis, Department of Psychology, Londres, The University of Sheffield (2001)
Grünwald, P.D.: In: Myung, J., Pitt, M.A. (consulted November 21, 2005), www.mitpress.mit.edu
Fikes, R.E., Nilsson, N.J.: STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving. In: Artificial Intelligence 2, United States (1971)
Friedman, N., Goldszmidt, M.: Learning Bayesian networks with local structure. In: Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (1996)
Kaebling, L.P.: Learning in embedded systems. A Bradford Book / MIT Press, Cambridge (1993)
Krause, P.J.: Learning probabilistic networks. The Knowledge Engineering Review 13(4), 321–351 (1998)
Lam, W., Segre, A.: A Distributed Learning Algorithm for Bayesian Inference Networks. In: IEEE Transactions on Knowledge and Data Engineering (2002)
McFarland, D.: Autonomy and Self-Sufficiency in Robots. In: Steels, L., Brooks, R. (eds.) The Artificial Route to Artificial Intelligence, Lawrence Erlbaum Associates, United States (1995)
Morales, E.: (consulted November 23, 2005), http://www.mor.itesm.mx/~rdec/node153.html
Murphy, K.: (consulted November 23, 2005), http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html
Newell, A.: The Knowledge Level, Technical report CMUCS-81-131, Carnegie Mellon University, United States (1991)
Nilsson, N.J.: Inteligencia Artificial. Una nueva síntesis. McGraw-Hill, España (2001)
Rissanen, J.: (2005, 11, 21), www.mdl-research.org
Russell, S.J., Norvig, P.: Artificial Intelligence. A Modern Approach. Prentice-Hall, United States (1996)
Sivakumar, K., Chen, R., Kargupta, H.: Learning Bayesian Network Structure from Distributed Data. In: Proceedings of the 3rd SIAM International Data Mining Conference, San Franciso (2003)
Sucar, L.E., Pérez-Brito, J., Ruiz Suárez, J.C., Morales, E.: Learning Structure from Data and its Application to Ozone Prediction. Applied Intelligence 7(4), 327–338 (1997)
Suzuki, J.: Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: Basic Properties. IEICE Trans. Fundamentals (1999)
Tannenbaum, A.S., Von Steen, M.: Distributed Systems. Principles and Paradigms. Prentice-Hall, USA (2002)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
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
Mondragón-Becerra, R. et al. (2006). Automatic Construction of Bayesian Network Structures by Means of a Concurrent Search Mechanism. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_62
Download citation
DOI: https://doi.org/10.1007/11925231_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49026-5
Online ISBN: 978-3-540-49058-6
eBook Packages: Computer ScienceComputer Science (R0)