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The Scientific World Journal
Volume 2014, Article ID 576409, 8 pages
http://dx.doi.org/10.1155/2014/576409
Research Article

Uncertainty Analysis of Knowledge Reductions in Rough Sets

1Department of Computer Science and Technology, Tongji University, 4800 Caoan Road, Shanghai 201804, China
2Department of Computer and Control Engineering, Yantai University, 32 Qingquan Road, Shandong 264005, China

Received 20 June 2014; Revised 3 August 2014; Accepted 3 August 2014; Published 27 August 2014

Academic Editor: Yunqiang Yin

Copyright © 2014 Ying Wang and Nan Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.