Uncertainty Analysis of Knowledge Reductions in Rough Sets
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-27
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Uncertainty analysis is a vital issue in intelligent information processing, especially in the age of big data.
Rough set theory has attracted much attention to this field since it was proposed.
Relative reduction is an important problem of rough set theory.
Different relative reductions have been investigated for preserving some specific classification abilities in various applications.
This paper examines the uncertainty analysis of five different relative reductions in four aspects, that is, reducts’ relationship, boundary region granularity, rules variance, and uncertainty measure according to a constructed decision table.
American Psychological Association (APA)
Wang, Ying& Zhang, Nan. 2014. Uncertainty Analysis of Knowledge Reductions in Rough Sets. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1050164
Modern Language Association (MLA)
Wang, Ying& Zhang, Nan. Uncertainty Analysis of Knowledge Reductions in Rough Sets. The Scientific World Journal No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1050164
American Medical Association (AMA)
Wang, Ying& Zhang, Nan. Uncertainty Analysis of Knowledge Reductions in Rough Sets. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1050164
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050164