International Journal of Computational Intelligence Systems

<Previous Article In Issue
Next Article In Issue>
Volume 10, Issue 1, 2017, Pages 277 - 292

Multigranulation rough set: A multiset based strategy

Authors
Xibei Yang1, 2, yangxibei@hotmail.com, Suping Xu1, supingxu@yahoo.com, Huili Dou1, *, douhuili@163.com, Xiaoning Song3, xnsong@yahoo.com.cn, Hualong Yu1, yuhualong@just.edu.cn, Jingyu Yang4, yangjy@mail.njust.edu.cn
1School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, P.R. China
2School of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094, P.R. China
3School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, P.R. China
4Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information (Nanjing University of Science and Technology), Ministry of Education, Nanjing, 210094, P.R. China
*Corresponding author: No. 2, Mengxi Road, School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, P.R. China.
Corresponding Author
Received 23 January 2015, Accepted 18 October 2016, Available Online 1 January 2017.
DOI
10.2991/ijcis.2017.10.1.19How to use a DOI?
Keywords
Approximate distribution reduct; Approximate quality; Multiset; Multiple multigranulation rough set
Abstract

A simple multigranulation rough set approach is to approximate the target through a family of binary relations. Optimistic and pessimistic multigranulation rough sets are two typical examples of such approach. However, these two multigranulation rough sets do not take frequencies of occurrences of containments or intersections into account. To solve such problem, by the motivation of the multiset, the model of the multiple multigranulation rough set is proposed, in which both lower and upper approximations are multisets. Such two multisets are useful when counting frequencies of occurrences such that objects belong to lower or upper approximations with a family of binary relations. Furthermore, not only the concept of approximate distribution reduct is introduced into multiple multigranulation rough set, but also a heuristic algorithm is presented for computing reduct. Finally, multiple multigranulation rough set approach is tested on eight UCI (University of California–Irvine) data sets. Experimental results show: 1. the approximate quality based on multiple multigranulation rough set is between approximate qualities based on optimistic and pessimistic multigranulation rough sets; 2. by comparing with optimistic and pessimistic multigranulation rough sets, multiple multigranulation rough set needs more attributes to form a reduct.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
10 - 1
Pages
277 - 292
Publication Date
2017/01/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.2017.10.1.19How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Xibei Yang
AU  - Suping Xu
AU  - Huili Dou
AU  - Xiaoning Song
AU  - Hualong Yu
AU  - Jingyu Yang
PY  - 2017
DA  - 2017/01/01
TI  - Multigranulation rough set: A multiset based strategy
JO  - International Journal of Computational Intelligence Systems
SP  - 277
EP  - 292
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2017.10.1.19
DO  - 10.2991/ijcis.2017.10.1.19
ID  - Yang2017
ER  -