Abstract
When adopting Bayesian network (BN) to represent and infer probabilistic causalities among multidimensional variables, the size of the conditional probability table (CPT) associated with each variable is doomed to be large, and the causality inferences cannot be done for arbitrary evidences. In this paper, we first extend the general BN by augmenting parameters for describing causalities among classes instead of specific instances of multidimensional variables. In the extended BN, called CBN, the CPT of a variable includes the probability of each class given parent classes, while a classifier of each variable is associated to determine the class that the given evidence belongs to. Further, we give the method for approximate inferences of the CBN for arbitrary evidences. Preliminary experiments verify the feasibility of our methods.
This work was supported by the National Natural Science Foundation of China (No. 60763007), the Natural Science Foundation of Yunnan Province (No. 2008CD083) and the Research Foundation of the Educational Department of Yunnan Province (No. 08Y0023).
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
Pedersen, T.B., Jensen, C.S.: Multidimensional data modeling for complex data. In: Proceedings of the 15th International Conference on Data Engineering (ICDE), pp. 336–345. IEEE Computer Society, Los Alamitos (1999)
Han, J., Kamber, M.: Data mining: Concepts and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)
Heckerman, D., Wellman, M.P.: Bayesian networks. Communication of the ACM 38(3), 27–30 (1995)
Pearl, J.: Probabilistic reasoning in intelligent systems: network of plausible inference. Morgan Kaufmann, San Mates (1988)
Buntine, W.L.: A guide to the literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering 8(2), 195–210 (1996)
Cheng, J., Greiner, R., Kelly, J., Bell, D., Liu, W.: Learning Bayesian network from data: An information-theory based approach. Artificial Intelligence 137(2), 619, 43–90 (2002)
Cheng, J.: PowerConstructor system (1998), http://www.cs.ualberta.ca/~jcheng/bnpc.htm
Russel, S.J., Norvig, P.: Artificial intelligence – a modern approach. Pearson Education, Publishing as Prentice-Hall (2002)
Cooper, G.F.: The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence 42(2-3), 393–405 (1990)
Pearl, J.: Evidential reasoning using stochastic simulation of causal models. Artificial Intelligence 32, 245–257 (1987)
Guo, H., Hsu, W.: A survey on algorithms for real-time Bayesian network inference. In: Proc. of the joint AAAI-02/KDD-02/UAI-02 workshop on Real-Time Decision Support and Diagnosis Systems (2002)
Norsys Software Corp. Netica 3.17 Bayesian network software from Norsys (2007), http://www.norsys.com
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Murthy, S.K.: Automatic construction of decision trees from data: A multi-disciplinary survey. Data Mining and Knowledge Discovery 2, 345–389 (1998)
Rastogi, R., Shim, K.: PUBLIC: A decision tree classifier that integrates building and pruning. In: Proceedings of the 14th International Conference on Data Engineering (ICDE), Florida, USA, pp. 404–415. IEEE Computer Society, Los Alamitos (1998)
Alsabti, K., Ranka, S., Singh, V.: CLOUDS: A decision tree classifier for large datasets. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD), New York, USA, pp. 2–8. AAAI Press, Menlo Park (1998)
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
Yue, K., Wei, MJ., Tian, KL., Liu, WY. (2009). Representing and Inferring Causalities among Classes of Multidimensional Data. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, QM. (eds) Advances in Data and Web Management. APWeb WAIM 2009 2009. Lecture Notes in Computer Science, vol 5446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00672-2_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-00672-2_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00671-5
Online ISBN: 978-3-642-00672-2
eBook Packages: Computer ScienceComputer Science (R0)