Monotonic Uncertainty Measures in Probabilistic Rough Set Model

2014 ◽  
pp. 88-98
Author(s):  
Guoyin Wang ◽  
Xi’ao Ma ◽  
Hong Yu
Keyword(s):  
Rough Set ◽  
Uncertainty Measures

scholarly journals Uncertainty measures of rough set prediction

1998 ◽  
Vol 106 (1) ◽  
pp. 109-137 ◽  
Author(s):  
Ivo Düntsch ◽  
Günther Gediga
Keyword(s):  
Rough Set ◽  
Uncertainty Measures

The uncertainty measures for covering rough set models

2020 ◽  
Vol 24 (16) ◽  
pp. 11909-11929
Author(s):  
Zhaohao Wang ◽  
Xiaoping Zhang ◽  
Jianping Deng
Keyword(s):  
Rough Set ◽  
Uncertainty Measures ◽  
Covering Rough Set

Uncertainty measures in multigranulation with different grades rough set based on dominance relation1

2016 ◽  
Vol 31 (2) ◽  
pp. 1133-1144 ◽  
Author(s):  
Jianhang Yu ◽  
Xiaoyan Zhang ◽  
Zhenhua Zhao ◽  
Weihua Xu
Keyword(s):  
Rough Set ◽  
Uncertainty Measures

Uncertainty Measures for Multigranulation Approximation Space

2015 ◽  
Vol 23 (03) ◽  
pp. 443-457 ◽  
Author(s):  
Guoping Lin ◽  
Jiye Liang ◽  
Yuhua Qian
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Information Entropy ◽  
Rough Set Theory ◽  
Mathematical Tool ◽  
Approximation Space ◽  
Uncertainty Measures ◽  
Multigranulation Rough Set ◽  
Essential Properties ◽  
The Relationship

Multigranulation rough set theory is a relatively new mathematical tool for solving complex problems in the multigranulation or distributed circumstances which are characterized by vagueness and uncertainty. In this paper, we first introduce the multigranulation approximation space. According to the idea of fusing uncertain, imprecise information, we then present three uncertainty measures: fusing information entropy, fusing rough entropy, and fusing knowledge granulation in the multigranulation approximation space. Furthermore, several essential properties (equivalence, maximum, minimum) are examined and the relationship between the fusion information entropy and the fusion rough entropy is also established. Finally, we prove these three measures are monotonously increasing as the partitions become finer. These results will be helpful for understanding the essence of uncertainty measures in multigranulation rough space and enriching multigranulation rough set theory.

A binary granule representation for uncertainty measures in rough set theory

2015 ◽  
Vol 28 (2) ◽  
pp. 867-878 ◽  
Author(s):  
Yumin Chen ◽  
Qingxin Zhu ◽  
Keshou Wu ◽  
Shunzhi Zhu ◽  
Zhiqiang Zeng
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Uncertainty Measures
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