Dynamic Maintenance Strategy for Approximations in Set-Valued Ordered Information Systems under the Attribute Generalization

被引：0

作者：

Luo, Chuan ^{[1
]}

Li, Tianrui ^{[1
]}

Chen, Hongmei ^{[1
]}

Liu, Dun ^{[2
]}

机构：

[1] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Peoples R China

[2] Southwest Jiaotong Univ, Sch Econ & Management, Chengdu 610031, Peoples R China

来源：

2012 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING (GRC 2012) | 2012年

基金：

美国国家科学基金会;

关键词：

rough set; approximations; incremental learning; information systems; ROUGH-SET; RULES;

D O I：

暂无

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 has been one of the major mathematical tools in data mining and knowledge discovery. The basic concepts of rough set theory are a pair of non-numerical operators, i.e., lower and upper approximation operators that are exported from the approximation spaces. Set-valued ordered information systems are generalized models of single-valued information systems. The attribute set in an information system may vary due to the arrival of new information. In this paper, we focus on the incremental approach for dynamically updating approximations in the set-valued ordered information systems when the attribute set varies over time.

引用

页码：332 / 337

页数：6

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