The dynamic update method of attribute-induced three-way granular concept in formal contexts

被引:30
作者
Long, Binghan [1 ]
Xu, Weihua [2 ]
Zhang, Xiaoyan [2 ]
Yang, Lei [1 ]
机构
[1] Chongqing Univ Technol, Sch Sci, Chongqing 400054, Peoples R China
[2] Southwest Univ, Coll Artificial Intelligence, Chongqing 400715, Peoples R China
基金
中国国家自然科学基金;
关键词
Attribute-induced; Dynamic formal context; Granular computing; The extension and connotation; Three-way granular concept; INFORMATION FUSION; DECISION; REDUCTION;
D O I
10.1016/j.ijar.2019.12.014
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 becoming a very hot research field, which has received extensive attention in recent years. It helps us to analyze and solve problems better by dividing complex problems into several simpler ones. Three-way granular concept is an important concept proposed by combining granular computing, formal concept analysis and three-way decision. Using traditional updating methods of three-way granular concepts, a lot of time and space resources are needed when multiple attributes or objects are deleted in formal context. In order to improve the efficiency and flexibility of obtaining three-way concepts, this paper discusses a novel dynamic update method of three-way granular concepts. In this paper, we firstly introduce the related knowledge of three-way granular concepts. Secondly, the update rules of the extension and connotation of attribute-induced three-way granular concepts are discussed in the dynamic formal context to construct three-way granular concepts. Moreover, we develop a method for establishing attribute-induced three-way granular concept by dynamic changes in the case of deleting multiple objects and attributes in the formal context. Furthermore, we design four algorithms to compare between the proposed approaches and traditional updating ways of three-way granular concepts. Finally, the validity of dynamic update method of attribute-induced three-way granular concept is verified through the experimental evaluation using six datasets coming from the University of California-Irvine (UCI) repository. (C) 2020 Elsevier Inc. All rights reserved.
引用
收藏
页码:228 / 248
页数:21
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