Information fusion in rough set theory : An overview

被引:108
|
作者
Wei, Wei [1 ]
Liang, Jiye [1 ]
机构
[1] Shanxi Univ, Sch Comp & Informat Technol, Minist Educ, Key Lab Computat Intelligence & Chinese Informat, Taiyuan 030006, Shanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Information fusion; Rough set; Multi-granulation; Multi-source; Multi-modality; Multi-scale; OPTIMAL SCALE SELECTION; ATTRIBUTE REDUCTION; RULE INDUCTION; KNOWLEDGE GRANULATION; PREFERENCE-RELATION; MULTIGRANULATION; APPROXIMATION; MODEL; ACQUISITION; UNCERTAINTY;
D O I
10.1016/j.inffus.2018.08.007
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Rough set theory is an efficient tool for dealing with inexact and uncertain information. Numerous studies have focused on rough set theory and associated methodologies, and in recent decades, various models and algorithms have been proposed. To clarify the application of information fusion in rough set theory, this paper presents an overview of existing information fusion approaches and methods for multi-source, multi-modality, multi-scale, and multi-view information systems from the perspective of objects, attributes, rough approximations, attribute reduction, and decision making. We provide a survey of recent applications of these theories and methods in various fields, and identify some potential challenges that require further research.
引用
收藏
页码:107 / 118
页数:12
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