Incremental approaches for updating reducts in dynamic covering information systems

被引：34

作者：

Lang, Guangming ^{[1
,2
,3
]}

Miao, Duoqian ^{[2
,3
]}

Cai, Mingjie ^{[4
]}

Zhang, Zhifei ^{[2
,3
]}

机构：

[1] Changsha Univ Sci & Technol, Sch Math & Stat, Changsha 410114, Hunan, Peoples R China

[2] Tongji Univ, Dept Comp Sci & Technol, Shanghai 201804, Peoples R China

[3] Tongji Univ, Key Lab Embedded Syst & Serv Comp, Minist Educ, Shanghai 201804, Peoples R China

[4] Hunan Univ, Coll Math & Econometr, Changsha 410004, Hunan, Peoples R China

来源：

KNOWLEDGE-BASED SYSTEMS | 2017年 / 134卷

基金：

中国国家自然科学基金; 高等学校博士学科点专项科研基金; 中国博士后科学基金;

关键词：

Characteristic matrix; Covering information system; Dynamic covering information system; Rough set; PROBABILISTIC ROUGH SETS; ATTRIBUTE REDUCTION; KNOWLEDGE REDUCTION; APPROXIMATIONS; MATRIX; MAINTENANCE; AXIOMATIZATION; ACQUISITION; MODEL;

D O I：

10.1016/j.knosys.2017.07.020

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In various real-world situations, there are actually a large number of dynamic covering information systems, and non-incremental learning technique is time consuming for updating approximations of sets in dynamic covering information systems. In this paper, we investigate incremental mechanisms of updating the second and sixth lower and upper approximations of sets in dynamic covering information systems with variations of attributes. Especially, we design effective algorithms for calculating the second and sixth lower and upper approximations of sets in dynamic covering information systems. The experimental results indicate that incremental algorithms outperform non-incremental algorithms in the presence of dynamic variation of attributes. Finally, we explore several examples to illustrate that the proposed approaches are feasible to perform knowledge reduction of dynamic covering information systems. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：85 / 104

页数：20

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