Abstract
A Bayesian network is a graphical model which analyzes probabilistic relationships among variables of interest. It has become a more and more popular and effective model for representing and inferring some process with uncertain information. Especially when it comes to the failure of uncertainty and correlation of complex equipment, and when the data is big. In this paper, we present an incremental approach for sparse Bayesian network structure learning. In order to analysis the correlation of heating load multidimensional feature factor, we use Bayesian network to establish the relationship between operating parameters of the heating units. Our approach builds upon previous research in sparse structure Gaussian Bayesian network, and because our project requires us to deal with a large amount of data with continuous parameters, we apply an incremental method on this model. Experimental results show that our approach is the efficient, effective, and accurate. The approach we propose can both deal with discrete parameters and continuous parameters, and has great application prospect in the big data field.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Borsuk, M.E., Stow, C.A., Reckhow, K.H.: A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis. Ecol. Model. 173, 219–239 (2004)
Agosta, J.M., Gardos, T.R., Druzdzel, M.J.: Query-based diagnostics. In: Jaeger, M., Nielsen, T.D. (eds.) Proceedings of the Fourth European Workshop on Probabilistic Graphical Models, PGM-08, Aalborg, Denmark, pp. 1–8 (2008)
Cooper, G., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9(4), 309–347 (1992)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction and Search. Springer, New York (1993). https://doi.org/10.1007/978-1-4612-2748-9
Cheng, J., Greiner, R., Kelly, J., et al.: Learning Bayesian networks from data: an information-theory based approach. Artif. Intell. 137(1–2), 325–331 (1997)
Zhang, H., Zhou, S., Zhang, K., et al.: Causal discovery using regression-based conditional independence tests. In: AAAI, pp. 1250–1256 (2017)
Alcobé, J.R.: Incremental hill-climbing search applied to Bayesian network structure learning, pp. 1320–1324 (2008)
Ko, S., Kim, D.W.: An efficient node ordering method using the conditional frequency for the K2 algorithm. Pattern Recognit. Lett. 40, 80–87 (2014)
Alonso-Barba, J.I., de la Ossa, L., Regnier-Coudert, O., et al.: Ant colony and surrogate tree-structured models for orderings-based Bayesian network learning. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 543–550. ACM (2015)
Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: the combination of knowledge and statistical data. Mach. Learn. 20(3), 197–243 (1995)
Dimkovski, M., An, A.: A Bayesian model for canonical circuits in the neocortex for parallelized and incremental learning of symbol representations. Neurocomputing 149(4), 1270–1279 (2015)
Yue, K., Fang, Q., Wang, X., et al.: A parallel and incremental approach for data-intensive learning of Bayesian networks. IEEE Trans. Cybern. 45(12), 2890–2904 (2017)
Yasin, A., Leray, P.: Incremental Bayesian network structure learning in high dimensional domains. In: International Conference on Modeling, Simulation and Applied Optimization, pp. 1–6. IEEE (2013)
Huang, S., et al.: A sparse structure learning algorithm for Gaussian Bayesian network identification from high-dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1328–1341 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sun, S. et al. (2018). An Incremental Approach for Sparse Bayesian Network Structure Learning. In: Xu, Z., Gao, X., Miao, Q., Zhang, Y., Bu, J. (eds) Big Data. Big Data 2018. Communications in Computer and Information Science, vol 945. Springer, Singapore. https://doi.org/10.1007/978-981-13-2922-7_24
Download citation
DOI: https://doi.org/10.1007/978-981-13-2922-7_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2921-0
Online ISBN: 978-981-13-2922-7
eBook Packages: Computer ScienceComputer Science (R0)
