DocumentCode
2555795
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
fYear
2007
fDate
11-14 Dec. 2007
Firstpage
544
Lastpage
549
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Asia-Pacific Service Computing Conference, The 2nd IEEE
Conference_Location
Tsukuba Science City
Print_ISBN
0-7695-3051-6
Type
conf
DOI
10.1109/APSCC.2007.78
Filename
4414507
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