|
|
|
| Attribute reduction in interval-valued information systems based on information entropies |
| Jian-hua Dai, Hu Hu, Guo-jie Zheng, Qing-hua Hu, Hui-feng Han, Hong Shi |
| School of Computer Science and Technology, Tianjin University, Tianjin 300350, China; College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China |
|
|
|
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.
|
|
Received: 13 December 2015
Published: 31 August 2016
|
|
|
区间值信息系统中基于信息熵的属性约简
概要：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。\n
关键词：
粗糙集理论,
区间值数据,
属性约简,
熵
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
