Title :
Modifications to Bayesian Rough Set Model and Rough Vague Sets
Author :
Li, Keqiu ; Yan, Deqin ; Qu, Wenyu
Author_Institution :
Dalian Univ. of Technol., Dalian
Abstract :
The variable precision rough set (VPRS) model generalizes the Pawlak rough set model with variable parameters. The Bayesian rough set (BRS) model improves the VPRS model with non-parametric modification by using the prior probability as a reference. This paper presents two research results related to rough set model and rough vague sets. One is a modification to the Bayesian rough set model and the other is a modification to rough vague sets. First, the Bayesian rough set model is analyzed and discussed. Second, a modification to this model is proposed and verified. Finally, a modification to rough vague sets is presented and its related properties are discussed..
Keywords :
Bayes methods; rough set theory; Bayesian rough set model; Pawlak rough set model; prior probability; rough vague sets; variable precision rough set; Application software; Bayesian methods; Computer science; Data analysis; Educational institutions; Fuzzy sets; Knowledge acquisition; Machine learning; Rough sets; Uncertainty;
Conference_Titel :
Asia-Pacific Service Computing Conference, The 2nd IEEE
Conference_Location :
Tsukuba Science City
Print_ISBN :
0-7695-3051-6
DOI :
10.1109/APSCC.2007.78
