Abstract
K2 is an algorithm used for learning the structure of a Bayesian networks (BN). The performance of the K2 algorithm depends on the order of the variables. If the given ordering is not sufficient, the score of the network structure is found to be low. We proposed a new variable ordering method in order to find the hierarchy of the variables. The proposed method was compared with other methods by using synthetic and real-world data sets. Experimental results show that the proposed method is efficient in terms of both time and score.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chickering, D.M.: Learning Bayesian networks is NP-complete. In: Learning from Data: Artificial Intelligence and Statistics, Springer, New York (1996)
Hruschka, E.R., Ebecken, N.F.F.: Towards efficient variables ordering for Bayesian networks classifier. Data Knowl. Eng. 63(2), 258–269 (2007). https://doi.org/10.1016/j.datak.2007.02.003
Larranaga, P., Kuijpers, C.M.H., Murga, R.H., Yurramendi, Y.: Learning Bayesian network structures by searching for the best ordering with genetic algorithms. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 26, 487–493 (1996)
Cowie, J., Oteniya, L., Coles, R.: Particle swarm optimization for learning Bayesian network. In: World Congress on Engineering, London, UK, pp. 71–76 (2007)
Hsu, W.H., Guo, H., Perry, B.B., Stilson, J.A.A.: Permutation genetic algorithm for variable ordering in learning Bayesian networks from data. In: Genetic and Evolutionary Computation Conference, New York, USA, pp. 383–390 (2002)
Acid, S., de Campos, L.M., Huete, J.F.: The search of causal orderings: a short cut for learning belief networks. In: Lecture Notes in Computer Science, vol. 2143, pp. 216–227 (2001)
Lee, J., Chung, W., Kim, E.: Structure learning of Bayesian networks using dual genetic algorithm. IEICE Trans. Inf. Syst. E91–D(1), 32–43 (2008). https://doi.org/10.1093/ietisy/e91-d.1.32
Akkoç, B.: The use of Bayesian network for social network analysis. Selçuk University (2012)
Russel, S.J., Norvig, P.: Articial Intelligence: A Modern Approach. Prentice-Hall, New Jersey (1995)
Pearl, J.: Bayesian networks: a model of self-activated memory for evidential Reasoning. In: 7th Cognitive Science Society, Irvine, ABD 1985, pp. 329–334 (1985)
Verma, T.S., Pearl, J.: Equivalence and synthesis of causal models. In: Uncertainty in Artificial Intelligence 6, USA, pp. 255–268 (1991)
Cooper, G.F., Heskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9, 309–347 (1992)
Chickering, D.M., Meek, C.: Finding optimal Bayesian networks. In: Eighteenth Conference on Uncertainty in Artificial Intelligence, Canada, pp. 94–102 (2002)
Spirtes, P., Glymour, C.: An algorithm for fast recovery of sparse causal graphs. Soc. Sci. Comput. Rev. 9, 62–72 (1991)
Kubica, J., Moore, A., Cohn, D., Schneider, J.: Finding underlying connections: a fast graph-based method for link analysis and collaboration queries. In: The Twentieth International Conference on Machine Learning, Washington DC, USA, pp. 392–399 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Uzbaş, B., Arslan, A. (2020). A New Variable Ordering Method for the K2 Algorithm. In: Hemanth, D., Kose, U. (eds) Artificial Intelligence and Applied Mathematics in Engineering Problems. ICAIAME 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 43. Springer, Cham. https://doi.org/10.1007/978-3-030-36178-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-36178-5_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36177-8
Online ISBN: 978-3-030-36178-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)