区间值信息系统中基于信息熵的属性约简

Jian-hua DAI; Hu HU; Guo-jie ZHENG; Qing-hua HU; Hui-feng HAN; Hong SHI

doi:10.1631/FITEE.1500447

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区间值信息系统中基于信息熵的属性约简

常规文章 | Updated：2022-05-19

- 区间值信息系统中基于信息熵的属性约简
- Attribute reduction in interval-valued information systems based on information entropies
- 信息与电子工程前沿（英文） 2016年17卷第9期页码：919-928
- Affiliations：
  
  1School of Computer Science and Technology, Tianjin University, Tianjin 300350, China
  2College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  E-mail:david.joshua@qq.com
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61473259, 61502335, 61070074, and 60703038), the Zhejiang Provincial Natural Science Foundation (No. Y14F020118), and the PEIYANG Young Scholars Program of Tianjin University, China (No. 2016XRX-0001)
- DOI：10.1631/FITEE.1500447
  中图分类号： TP18
- 收稿：2015-12-13，
  
  录用：2016-3-4，
  
  纸质出版：2016-09
- Accepted：
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区间值信息系统中基于信息熵的属性约简[J]. 信息与电子工程前沿（英文）, 2016,17(9):919-928.

Jian-hua DAI, Hu HU, Guo-jie ZHENG, et al. Attribute reduction in interval-valued information systems based on information entropies[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(9): 919-928.
区间值信息系统中基于信息熵的属性约简[J]. 信息与电子工程前沿（英文）, 2016,17(9):919-928. DOI： 10.1631/FITEE.1500447.

Jian-hua DAI, Hu HU, Guo-jie ZHENG, et al. Attribute reduction in interval-valued information systems based on information entropies[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(9): 919-928. DOI： 10.1631/FITEE.1500447.

摘要

区间值数据用来表示包含观察值的不确定性。区间值信息系统的处理有助于拓展粗糙集理论的应用范畴。属性约简是区间值数据分析的一个关键问题。现有针对传统单值数据的方法不适用于区间值数据。目前，关注区间值数据约简的研究还相对较少。本文从信息论的角度提出了一个区间值数据的属性约简框架，定义了区间值信息系统中的熵、条件熵以及联合熵等概念，继而构造了属性约简算法。实验结果表明所构造的方法是有效的。

Abstract

Interval-valued data appear as a way to represent the uncertainty affecting the observed values. Dealing with interval-valued information systems is helpful to generalize the applications of rough set theory. Attribute reduction is a key issue in analysis of interval-valued data. Existing attribute reduction methods for single-valued data are unsuitable for interval-valued data. So far

there have been few studies on attribute reduction methods for interval-valued data. In this paper

we propose a framework for attribute reduction in interval-valued data from the viewpoint of information theory. Some information theory concepts

including entropy

conditional entropy

and joint entropy

are given in interval-valued information systems. Based on these concepts

we provide an information theory view for attribute reduction in interval-valued information systems. Consequently

attribute reduction algorithms are proposed. Experiments show that the proposed framework is effective for attribute reduction in interval-valued information systems.

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references

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