Theory And Approach Of Reduction Of Bayesian Rough Set

Rough set theory, proposed by Pawlak in the early 1980s, is a mathe-matical theory for reasoning about data. The main idea of the theory is to approximate inexact, uncertain concepts by using of available knowledge or information. Since 1990s, it has attrated much attention of researchers around the world, and has been well developed and applied. Now, this theory has become a flash point in the research area of computer science and information science. With the twenty years'development, Rough set theory has been found to have very successful applications in the fields of artificial intelligence such as machine learning, pattern recognition,decicion analysis, process contral,knowledge discovery in databases, and expert sys-tems.In this study, Bayesian rough set model and variable precision Bayesian rough set model are further studied. Firstly, the native of Bayesian rough set and variable precision bayesian rough set is given. Secondly, corre-sponding toβupper(lower) distribution reduction in variable precision rough set model,the study probes intoβupper(lower) distribution re-duction in Bayesian rough set and variable precision bayesian rough set, and meanwhile discusses the relationship between them. A upper(lower) distribution reduction is the minimum attribute set keeping each decision-making class the same approximation. On the basis of the above, it re-ceives the judgement theorems and discernibility attribute matrixes of the upper(lower) distribution knowledge reduction and provides the method of the target information system knowledge reduction. Finally, the study uses some examples to prove the feasibility and operability of the method, which is meaningful and valuable both in the theory and in applications.