A survey on granular computing and its uncertainty measure from the perspective of rough set theory

被引：23

作者：

Cheng, Yunlong ^{[1
]}

Zhao, Fan ^{[1
,2
]}

Zhang, Qinghua ^{[1
,2
]}

Wang, Guoyin ^{[1
]}

机构：

[1] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Chongqing 400065, Peoples R China

[2] Chongqing Univ Posts & Telecommun, Sch Sci, Chongqing 400065, Peoples R China

来源：

GRANULAR COMPUTING | 2021年 / 6卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Granular computing; Rough sets; Granules; Uncertainty measure; Approximation sets; ATTRIBUTE REDUCTION; INFORMATION ENTROPY; DECISION-MAKING; CLASSIFICATION; MODEL; APPROXIMATION; KNOWLEDGE; SELECTION; SYSTEMS; METHODOLOGY;

D O I：

10.1007/s41066-019-00204-3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Granular computing is an umbrella term to cover a series of theories, methodologies, techniques, and tools that make use of information granules in complex problem solving. Rough sets, as one of the main concrete models of granular computing, has attracted considerable attention and has been successfully applied to numerous kinds of fields. To show the basic ideas and principles of granular computing from the perspective of rough sets, the main models, uncertainty measures and applications of rough sets are surveyed in the paper.

引用

页码：3 / 17

页数：15

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