基于直觉模糊粒化的信息熵属性约简算法

陈曦

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基于直觉模糊粒化的信息熵属性约简算法

更新时间：2026-09-04

- 基于直觉模糊粒化的信息熵属性约简算法
- Attribute reduction algorithm based on the information entropy of intuitionstic fuzzy granulation
- 微电子学与计算机 2020年37卷第1期页码：38-45
- 作者机构：
  
  1. 华中科技大学管理学院,湖北,武汉,430074
  2. 武汉铁路职业技术学院机构,湖北,武汉,430200
- 作者简介：
- 基金信息：
  
  国家自然科学基金 71277097
- DOI：
  中图分类号：
- 纸质出版：2020
- 稿件说明：
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陈曦. 基于直觉模糊粒化的信息熵属性约简算法[J]. 微电子学与计算机, 2020, 37(1): 38-45.

CHEN Xi. Attribute reduction algorithm based on the information entropy of intuitionstic fuzzy granulation[J]. 2020, 37(1): 38-45.
陈曦. 基于直觉模糊粒化的信息熵属性约简算法[J]. 微电子学与计算机, 2020, 37(1): 38-45. DOI：

CHEN Xi. Attribute reduction algorithm based on the information entropy of intuitionstic fuzzy granulation[J]. 2020, 37(1): 38-45. DOI：

摘要

在直觉模糊关系中，对象之间通过隶属度和非隶属度的刻画使得拥有了更为优越的关系评估效果.为了对信息系统的不确定性达到更好的度量，首先引入基于直觉模糊关系对信息系统进行直觉模糊粒化，然后在粒化的结果中依据隶属度和非隶属度分别定义了信息熵的概念，并将它们结合作为直觉模糊关系下信息系统的信息熵，最后根据该信息熵构造一种属性约简算法.实验结果表明提出的算法具有较优的属性约简性能.

Abstract

In intuitionistic fuzzy relations

between objects have more superior relationship evaluation effect through the description of membership degree and non-membership degree. In order to achieve better measurement for information system. Firstly

the intuitionistic fuzzy granulation for information system based on intuitionistic fuzzy relations is introduced in this paper. And then

the concepts of entropy are respectively defined according to membership degree and non-membership degree in granulation results

in addition

combining them as the information entropy of the information system under the intuitionistic fuzzy relation. Finally

an attribute reduction algorithm is constructed according to the information entropy. Experimental results show that the proposed algorithm has better attribute reduction performance.

关键词

Keywords

references

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