Abstract
Bayesian network (BN) is a directed acyclic graph encoding probabilistic independence statements between variables. BN with decision attribute as a root can be applied to classification of new cases, by synthesis of conditional probabilities propagated along the edges. We consider approximate BNs, which almost keep entropy of a decision table. They have usually less edges than classical BNs. They enable to model and extend the well-known Naive Bayes approach. Experiments show that classifiers based on approximate BNs can be very efficient.
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References
Bay, S.D.: The UCI Machine Learning Repository, http://www.ics.uci.edu/ml
Box, G.E.P., Tiao, G.C.: Bayesian Inference in Statistical Analysis. Wiley (1992).
Buntine, W.: A guide to the literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering (1996).
Kapur, J.N., Kesavan, H.K.: Entropy Optimization Principles with Applications. Academic Press (1992).
Pawlak, Z.: Rough sets-Theoretical aspects of reasoning about data. Kluwer Academic Publishers (1991).
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann (1988).
Ślçezak, D.: Approximate Bayesian networks. In: B. Bouchon-Meunier, J. Gutierrez-Rios, L. Magdalena, R.R. Yager (eds), Technologies for Contructing Intelligent Systems 2: Tools. Springer-Verlag (2002) pp. 313–326.
Ślçezak, D., Wróblewski, J.: Order-based genetic algorithms for extraction of approximate bayesian networks from data. In: Proc. of IPMU’2002. France (2002).
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Slçezak, D., Wróblewski, J. (2002). Approximate Bayesian Network Classifiers. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_48
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DOI: https://doi.org/10.1007/3-540-45813-1_48
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