Full Text:   <2358>

Summary:  <1858>

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: 6041

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jian-hua Dai

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

-   Go to

Article info.
Open peer comments

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.

@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.

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

Reference

[1]Billard, L., Douzal-Chouakria, A., Diday, E., 2008. Symbolic Principal Component for Interval-Valued Observations. Available from https://hal.archives-ouvertes.fr/hal-00361053.

[2]Bustince, H., Barrenechea, E., Pagola, M., 2006. Restricted equivalence functions. Fuzzy Sets Syst., 157(17):2333-2346.

[3]Dai, J.H., 2008. Rough 3-valued algebras. Inform. Sci., 178(8):1986-1996.

[4]Dai, J.H., Tian, H.W., 2013. Fuzzy rough set model for set-valued data. Fuzzy Sets Syst., 229:54-68.

[5]Dai, J.H., Xu, Q., 2012. Approximations and uncertainty measures in incomplete information systems. Inform. Sci., 198:62-80.

[6]Dai, J.H., Xu, Q., 2013. Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Appl. Soft Comput., 13(1):211-221.

[7]Dai, J.H., Wang, W.T., Xu, Q., et al., 2012. Uncertainty measurement for interval-valued decision systems based on extended conditional entropy. Knowl.-Based Syst., 27:443-450.

[8]Dai, J.H., Tian, H.W., Wang, W.T., et al., 2013a. Decision rule mining using classification consistency rate. Knowl.-Based Syst., 43:95-102.

[9]Dai, J.H., Wang, W.T., Mi, J.S., 2013b. Uncertainty measurement for interval-valued information systems. Inform. Sci., 251:63-78.

[10]Dai, J.H., Wang, W.T., Xu, Q., 2013c. An uncertainty measure for incomplete decision tables and its applications. IEEE Trans. Cybern., 43(4):1277-1289.

[11]Galar, M., Fernandez, J., Beliakov, G., et al., 2011. Interval-valued fuzzy sets applied to stereo matching of color images. IEEE Trans. Image Process., 20(7):1949-1961.

[12]Hedjazi, L., Aguilar-Martin, J., Le Lann, M.V., 2011. Similarity-margin based feature selection for symbolic interval data. Patt. Recogn. Lett., 32(4):578-585.

[13]Hu, Y.C., 2015. Flow-based tolerance rough sets for pattern classification. Appl. Soft Comput., 27:322-331.

[14]Kryszkiewicz, M., 1998. Rough set approach to incomplete information systems. Inform. Sci., 112(1-4):39-49.

[15]Leung, Y., Fischer, M.M., Wu, W.Z., et al., 2008. A rough set approach for the discovery of classification rules in interval-valued information systems. Int. J. Approx. Reason., 47(2):233-246.

[16]Lin, S.H., Huang, C.C., Che, Z.X., 2015. Rule induction for hierarchical attributes using a rough set for the selection of a green fleet. Appl. Soft Comput., 37:456-466.

[17]Pawlak, Z., 1991. Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht & Boston.

[18]Qian, Y.H., Liang, J.Y., Dang, C.Y., 2008. Interval ordered information systems. Comput. Math. Appl., 56(8):1994-2009.

[19]Yang, X.B., Yu, D.J., Yang, J.Y., et al., 2009. Dominance-based rough set approach to incomplete interval-valued information system. Data Knowl. Eng., 68(11):1331-1347.

[20]Zhang, C.Y., Fu, H.Y., 2006. Similarity measures on three kinds of fuzzy sets. Patt. Recogn. Lett., 27(12):1307-1317.

[21]Zhang, X.H., Dai, J.H., Yu, Y.C., 2015. On the union and intersection operations of rough sets based on various approximation spaces. Inform. Sci., 292:214-229.

[22]Zhang, X.H., Miao, D.Q., Liu, C.H., et al., 2016. Constructive methods of rough approximation operators and multigranulation rough sets. Knowl.-Based Syst., 91:114-125.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE