[1]LIANG Meishe,MI Jusheng,ZHANG Shaopu.Generalized intuitionistic fuzzy probabilistic rough set models on two universes and their applications[J].CAAI Transactions on Intelligent Systems,2022,17(3):585-592.[doi:10.11992/tis.202106025]
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Generalized intuitionistic fuzzy probabilistic rough set models on two universes and their applications

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 3 Page number: 585-592 Column: 学术论文—人工智能基础 Public date: 2022-05-05
Title:
Generalized intuitionistic fuzzy probabilistic rough set models on two universes and their applications
Author(s):
LIANG Meishe1;  MI Jusheng2;  ZHANG Shaopu1
1. Department of Mathematics and Physics, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;
2. School of Mathematics Science, Hebei Normal University, Shijiazhuang 050024, China
Keywords:
two universes; rough set; intuitionistic fuzzy relations; probabilistic rough set; intuitionistic fuzzy sets; intuitionistic fuzzy probabilistic approximation spaces; lower approximation; upper approximation
CLC:
TP391
DOI:
10.11992/tis.202106025
Abstract:
In the existing intuitionistic fuzzy (IF) probabilistic rough set model of two universes, the probabilistic rough lower and upper approximations of a classical set under IF binary relation are discussed by setting two thresholds, ${\lambda _1}\;{\rm{and}}\;{\lambda _2}$. However, this model is unable to calculate the probabilistic rough lower and upper approximations of IF sets under IF binary relation, which limits the application of this model to a certain extent. In this study, we first define the IF conditional probability. Then, the generalized IF probability rough set models are constructed in IF probabilistic approximation space. In addition, the main properties of the model are discussed. Finally, the numerical examples of clinical diagnostic systems are applied to illustrate the validity of the proposed models. Compared with other models, the proposed generalized IF probabilistic rough set models further enrich the probabilistic rough set theory and are more suitable for practical applications.
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