Abstract
It is important to find a suitable loss function and thus produce a realistic decision-making rules in the minimum risk of Bayesian decision-making process. Generally, there is no stringent condition in the decision-making process, and if we add constraints of the risk of loss, the risk of the error will be reduced. We discuss the basic process of minimum risk of Bayesian decision, and set up the probabilistic rough set model with variable precision on Bayesian decision. The model could decrease the error risk of the Bayesian decision.
Supported by Scientific Research Guiding Plan Project of Tangshan in Hebei Province (No. 09130205a).
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References
Wenxiu, Z.: Rough Set Theory and Methods. Science Press, Beijing (2001)
Idzislaw, P.: Rough set theory and its applications to data analysis. Cyberaetics and Systems (1998)
Meiping, X.: Laplace distribution parameter estimation of loss function and the risk that the Bayes function. Statistics and Decision-Making (2010)
Li, H.X., Yao, Y.Y., Zhou, X.Z., Huang, B.: Two-Phase Rule Induction from Incomplete Data. In: Wang, G., et al. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 47–54. Springer, Heidelberg (2008)
Wojciech, I.: Variable precision rough set mode of Computer and System Sciences (2007)
Wang, G.Y., Zhang, Q.H.: Uncertainty of rough sets in different knowledge granularities. Chinese Journal of Computers (2008)
Yueling, Z.: Based on variable precision rough set threshold. Control and Decision (2007)
Bingzhen, S.: Probabilistic rough set model with variable precision. Northwest Normal University (2005)
Ning, S., Hongchao, M.: Pattern Recognition Theory and Method. Wuhan University Press, Wuhan (2004)
Huaizhong, Z.: Bayes decision-making Probabilistic rough set model. Small Micro-Computer System (2004)
Meiping, X.: Laplace distribution parameter estimation of loss function and the risk that the Bayes function. Statistics and Decision-Making (2010)
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Li, L., Wang, J., Jiang, J. (2010). Bayesian Decision Model Based on Probabilistic Rough Set with Variable Precision. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds) Information Computing and Applications. ICICA 2010. Communications in Computer and Information Science, vol 105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16336-4_5
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DOI: https://doi.org/10.1007/978-3-642-16336-4_5
Publisher Name: Springer, Berlin, Heidelberg
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