Abstract
Bayesian network is a directed acyclic graph. Existing Bayesian network learning approaches based on search & scoring usually work with a heuristic search for finding the highest scoring structure. This paper describes a new data mining algorithm to learn Bayesian networks structures based on an immune binary particle swarm optimization (IBPSO) method and the Minimum Description Length (MDL) principle. IBPSO is proposed by combining the immune theory in biology with particle swarm optimization (PSO). It constructs an immune operator accomplished by two steps, vaccination and immune selection. The purpose of adding immune operator is to prevent and overcome premature convergence. Experiments show that IBPSO not only improves the quality of the solutions, but also reduces the time cost.
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
Suzuki, J.: A construction of Bayesian networks from databases based on a MDL scheme. In: Proc of the 9th Conference of Uncertainty in Artificial Intelligence, pp. 266–273. Morgan Kaufmann, San Mateo (1993)
Xiang, Y., Wong, S.K.M.: Learning conditional independence relations from a probabilistic model, Department of Computer Science, University of Regina, CA, Tech Rep: CS-94-03 (1994)
Heckerman, D.: Learning Bayesian network: The combination of knowledge and statistic data. Machine Learning 20(2), 197–243 (1995)
Cheng, J., Greiner, R., Kelly, J.: Learning Bayesian networks from data: An efficient algorithm based on information theory. Artificial Intelligence 137(1-2), 43–90 (2002)
Lam, W., Bacchus, F.: Learning Bayesian belief networks: An algorithm based on the MDL principle. Computational Intelligence 10(4) (1994)
Larrañaga, P., Poza, M., Yurramendi, Y., Murga, R., Kuijpers, C.: Structure Learning of Bayesian Network by Genetic Algorithms: A Performance Analysis of Control Parameters. IEEE Trans. Pattern Analysis and Machine Intelligence 18(9), 912–926 (1996)
Chickering, D.M.: Learning Bayesian networks is NP-complete. In: Fisher, D., Lenz, H.J. (eds.) Learning from Data: Artificial Intelligence and Statistics V, pp. 121–130. Springer, Berlin (1996)
Lam, W., Bacchus, F.: Learning Bayesian belief networks: an algorithm based on the MDL principle. Computational Intelligence 10(4), 269–293 (1994)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of IEEE International Conference of Evolutionary Computation, Anchorage, Alaska, May 1998, pp. 69–73 (1998)
Kennedy, J., Eberhart, R.: A discrete binary version of the particle swarm optimization algorithm. In: Proceedings of the Conference on Systems, Man, and Cybernetics, pp. 4104–4109 (1997)
Jiao, L.C., Wang, L.: A novel genetic algorithm based on Immunity. IEEE Trans. on Systems, Man, and Cybernetics-Part A Systems and Humans 30(5), 552–561 (2000)
Lu, G., Tan, D.j.: Improvement on regulating definition of antibody density of immune algorithm. In: Proceeding of the 9th international conference on neural information processing, vol. 5, pp. 2669–2672 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, XL., He, XD., Chen, CM. (2009). A Method for Learning Bayesian Networks by Using Immune Binary Particle Swarm Optimization. In: Ślęzak, D., Kim, Th., Zhang, Y., Ma, J., Chung, Ki. (eds) Database Theory and Application. DTA 2009. Communications in Computer and Information Science, vol 64. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10583-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-10583-8_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10582-1
Online ISBN: 978-3-642-10583-8
eBook Packages: Computer ScienceComputer Science (R0)