Uncertainty Analysis of Knowledge Reductions in Rough Sets
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 i...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/576409 |
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| _version_ | 1832558874474840064 |
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| author | Ying Wang Nan Zhang |
| author_facet | Ying Wang Nan Zhang |
| author_sort | Ying Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-81c51a718cf244dcb58757f3f94aa95b |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-81c51a718cf244dcb58757f3f94aa95b2025-02-03T01:31:27ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/576409576409Uncertainty Analysis of Knowledge Reductions in Rough SetsYing Wang0Nan Zhang1Department of Computer Science and Technology, Tongji University, 4800 Caoan Road, Shanghai 201804, ChinaDepartment of Computer and Control Engineering, Yantai University, 32 Qingquan Road, Shandong 264005, ChinaUncertainty 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.http://dx.doi.org/10.1155/2014/576409 |
| spellingShingle | Ying Wang Nan Zhang Uncertainty Analysis of Knowledge Reductions in Rough Sets The Scientific World Journal |
| title | Uncertainty Analysis of Knowledge Reductions in Rough Sets |
| title_full | Uncertainty Analysis of Knowledge Reductions in Rough Sets |
| title_fullStr | Uncertainty Analysis of Knowledge Reductions in Rough Sets |
| title_full_unstemmed | Uncertainty Analysis of Knowledge Reductions in Rough Sets |
| title_short | Uncertainty Analysis of Knowledge Reductions in Rough Sets |
| title_sort | uncertainty analysis of knowledge reductions in rough sets |
| url | http://dx.doi.org/10.1155/2014/576409 |
| work_keys_str_mv | AT yingwang uncertaintyanalysisofknowledgereductionsinroughsets AT nanzhang uncertaintyanalysisofknowledgereductionsinroughsets |