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CLC number: TP18

On-line Access: 2016-08-31

Received: 2015-12-13

Revision Accepted: 2016-03-04

Crosschecked: 2016-08-08

Cited: 4

Clicked: 5642

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jian-hua Dai

http://orcid.org/0000-0003-1459-0833

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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.9 P.919-928

http://doi.org/10.1631/FITEE.1500447


Attribute reduction in interval-valued information systems based on information entropies


Author(s):  Jian-hua Dai, Hu Hu, Guo-jie Zheng, Qing-hua Hu, Hui-feng Han, Hong Shi

Affiliation(s):  School of Computer Science and Technology, Tianjin University, Tianjin 300350, China; more

Corresponding email(s):   david.joshua@qq.com

Key Words:  Rough set theory, Interval-valued data, Attribute reduction, Entropy


Jian-hua Dai, Hu Hu, Guo-jie Zheng, Qing-hua Hu, Hui-feng Han, Hong Shi. Attribute reduction in interval-valued information systems based on information entropies[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(9): 919-928.

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author="Jian-hua Dai, Hu Hu, Guo-jie Zheng, Qing-hua Hu, Hui-feng Han, Hong Shi",
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%A Guo-jie Zheng
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T1 - Attribute reduction in interval-valued information systems based on information entropies
A1 - Jian-hua Dai
A1 - Hu Hu
A1 - Guo-jie Zheng
A1 - Qing-hua Hu
A1 - Hui-feng Han
A1 - Hong Shi
J0 - Frontiers of Information Technology & Electronic Engineering
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DOI - 10.1631/FITEE.1500447


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.

The authors present an attribute reduction model for interval-valued attributes. The paper deals with an interesting topic and the proposed approach is interesting. The paper is well written and its structure is good.

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。
关键词:粗糙集理论;区间值数据;属性约简;熵

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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