CLC number: TP18
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2016-08-08
Cited: 4
Clicked: 6207
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.
@article{title="Attribute reduction in interval-valued information systems based on information entropies",
author="Jian-hua Dai, Hu Hu, Guo-jie Zheng, Qing-hua Hu, Hui-feng Han, Hong Shi",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="17",
number="9",
pages="919-928",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500447"
}
%0 Journal Article
%T Attribute reduction in interval-valued information systems based on information entropies
%A Jian-hua Dai
%A Hu Hu
%A Guo-jie Zheng
%A Qing-hua Hu
%A Hui-feng Han
%A Hong Shi
%J Frontiers of Information Technology & Electronic Engineering
%V 17
%N 9
%P 919-928
%@ 2095-9184
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500447
TY - JOUR
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
VL - 17
IS - 9
SP - 919
EP - 928
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
ER -
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.
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