Affiliations: School of Mathematics, Physics and Information Science, Zhejiang Ocean University, Zhoushan, Zhejiang 316004, P. R. China. [email protected]
Note: [] Address for correspondence: School of Mathematics, Physics and Information Science, Zhejiang Ocean University, Zhoushan, Zhejiang 316004, P. R. China. E-mail address: [email protected]; tel.: +86 580 8180928; fax: +86 580 2550029. This work was supported by grants from the National Natural Science Foundation of China (Nos. 61075120 and 61173181).
Abstract: Statisticians and database users often encounter the problem of missing or imprecise data obtained by a random experiment. Such a data set is called a random incomplete information table. In this paper, we study knowledge reduction in random incomplete information tables and random incomplete decision tables by using a hybrid model based on the rough set theory and the Dempster-Shafer theory of evidence. The concepts of random belief reducts and random plausibility reducts in random incomplete information tables and random incomplete decision tables are introduced. The relationships among the lower approximation reduct, the upper approximation reduct, the random belief reduct, the random plausibility reduct, and the classical reduct in random incomplete decision tables are examined.
Keywords: Belief functions, knowledge reduction, random incomplete decision tables, random incomplete information tables, random sets, rough sets
