RESEARCH ON ATTRIBUTE REDUCTION ALGORITHM IN COVERING GRANULAR COMPUTING MODEL

被引：0

作者：

Zhou, Jia-Qing ^{[1
]}

Nie, Hong-Mei ^{[2
]}

机构：

[1] Zhejiang Normal Univ, Xingzhi Coll, Jinhua 321004, Zhejiang, Peoples R China

[2] Zhejiang Normal Univ, Coll Math Phys & Informat Engn, Jinhua 321004, Zhejiang, Peoples R China

来源：

PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 2 | 2018年

关键词：

Granular computing; Covering rough set; Attribute reduction; Covering related families; DECISION SYSTEMS;

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：

The granular computing model is a very important model in data mining. Seven kinds of models have been proposed for the covering granular computing model. Among these models, the third kind of models was considered to be more reasonable model. However, there are not many solutions to attribute reduction based on the third kind of covering models. Aiming at the third kind of covering granular computing models, this paper discusses the concepts of covering related families and simplified covering related families, and gives a simplified calculation method for covering related families, and then calculates all attribute reductions from the simplified covering related families. Then, a heuristic method of attribute reduction is discussed. The feasibility of the proposed method is verified by an example, which provides a new method for attribute reduction in data mining.

引用

页码：366 / 369

页数：4

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