Stepwise Structure Learning Using Probabilistic Pruning for Bayesian Networks: Improving Efficiency and Comparing Characteristics
Conference paper
First Online:
Abstract
This paper evaluates a structure learning method for Bayesian networks called Stepwise Structure Learning with Probabilistic pruning (SSL-Pro). Probabilistic pruning allows this method to obtain appropriate network structures while reducing computational time for structure learning. Computer experiments were conducted to investigate the characteristics of the SSL-Pro. Results showed that the SSL-Pro generally provided favorable performance, and revealed several parameter-setting guidelines to ensure reasonable learning.
References
- 1.Cheng, J., Greiner, R., Kelly, J., Bell, D., Liu, W.: Learning Bayesian networks from data: an information-theory based approach. Artif. Intell. 137, 43–90 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
- 2.Larranaga, P., Poza, M., Yurramendi, Y., Murga, R.H., Kuijpers, C.M.H.: Structure learning of bayesian networks by genetic algorithms: a performance analysis of control parameters. IEEE TPAMI 18, 912–926 (1996)CrossRefGoogle Scholar
- 3.Fung, R., Chang, K.: Weighing and integrating evidence for stochastic simulation in bayesian networks. In: 5th Annual Conference on Uncertainty in Artificial Intelligence (UAI 1989), pp. 209–219. Elsevier Science (1989)Google Scholar
- 4.Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, Burlingto (1988)zbMATHGoogle Scholar
- 5.Fukui, H., Kitakoshi, D.: Prior knowledge-based stepwise structure learning of bayesian networks (in japanese). IEICE Technical report, vol. 108, pp. 55–60 (2009)Google Scholar
- 6.Nishiyama, H., Kitakoshi, D., Suzuki, M.: A study on appropriate parameter settings in a stepwise structure learning for bayesian networks (in japanese). In: Proceeding 38th SICE Symposium on Intelligent Systems, pp. 79–84 (2011)Google Scholar
- 7.Kitakoshi, D., Azuma, G., Suzuki, M.: Improving learning speed in stepwise structure learning method for bayesian networks by using probabilistic pruning (in japanese). IPSJ SIG Technical report, vol. 2016-ICS-182, pp. 1–6 (2016)Google Scholar
- 8.Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Upper Saddle River (1988)zbMATHGoogle Scholar
- 9.Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
- 10.Marco, S.: bnlearn (2016). www.bnlearn.com/bnrepository/
- 11.Rissanen, J.: Stochastic Complexity in Statistical Inquiry. World Scientific Publishing Co., Singapore (1989)zbMATHGoogle Scholar
Copyright information
© Springer Nature Singapore Pte Ltd. 2017