Maximum Consistent Block Based Variable Precision Rough Set Model and Attribute Reduction
In this paper, attribute reduction of incomplete information system is studied. Firstly, optimistic and pessimistic generalized variable precision rough set models based on maximal consistent blocks are constructed. The relationship between the two models and their main properties are analyzed. Afte...
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2020-05-01
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doaj-2d3ef57bbd0843239e2e31e939e4fea02021-08-10T04:08:16ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182020-05-0114589290010.3778/j.issn.1673-9418.1905011Maximum Consistent Block Based Variable Precision Rough Set Model and Attribute ReductionSUN Yan, MI Jusheng, FENG Tao, LI Leijun, LIANG Meishe01. College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050024, China 2. School of Science, Hebei University of Science and Technology, Shijiazhuang 050018, China 3. Department of Scientific Development and School-Business Cooperation, Shijiazhuang University of Applied Technology, Shijiazhuang 050081, ChinaIn this paper, attribute reduction of incomplete information system is studied. Firstly, optimistic and pessimistic generalized variable precision rough set models based on maximal consistent blocks are constructed. The relationship between the two models and their main properties are analyzed. After that β-lower optimistic (pessimistic) and β-upper distribution attribute reduction is defined, and the corresponding judgement theorem is given. Boolean method of attribute reduction is obtained, and it can keep the upper (lower) approximation distribution of the decision class unchanged. This method of constructing discernibility set between maximal consistent blocks reduces the size of the discernibility matrix, and the process of computing attribute reduction is simplified, which can effectively save computing time and storage space. Then two examples of incomplete information systems with “lost” “don't care” values and only “don't care” values are employed to illustrate the proposed method. Finally, 5 sets of incomplete information data sets from UCI data set are used to validate its effectiveness.http://fcst.ceaj.org/CN/abstract/abstract2202.shtmlmaximal consistent blockvariable precision rough set modelattribute reductionincomplete information system |
collection |
DOAJ |
language |
zho |
format |
Article |
sources |
DOAJ |
author |
SUN Yan, MI Jusheng, FENG Tao, LI Leijun, LIANG Meishe |
spellingShingle |
SUN Yan, MI Jusheng, FENG Tao, LI Leijun, LIANG Meishe Maximum Consistent Block Based Variable Precision Rough Set Model and Attribute Reduction Jisuanji kexue yu tansuo maximal consistent block variable precision rough set model attribute reduction incomplete information system |
author_facet |
SUN Yan, MI Jusheng, FENG Tao, LI Leijun, LIANG Meishe |
author_sort |
SUN Yan, MI Jusheng, FENG Tao, LI Leijun, LIANG Meishe |
title |
Maximum Consistent Block Based Variable Precision Rough Set Model and Attribute Reduction |
title_short |
Maximum Consistent Block Based Variable Precision Rough Set Model and Attribute Reduction |
title_full |
Maximum Consistent Block Based Variable Precision Rough Set Model and Attribute Reduction |
title_fullStr |
Maximum Consistent Block Based Variable Precision Rough Set Model and Attribute Reduction |
title_full_unstemmed |
Maximum Consistent Block Based Variable Precision Rough Set Model and Attribute Reduction |
title_sort |
maximum consistent block based variable precision rough set model and attribute reduction |
publisher |
Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
series |
Jisuanji kexue yu tansuo |
issn |
1673-9418 |
publishDate |
2020-05-01 |
description |
In this paper, attribute reduction of incomplete information system is studied. Firstly, optimistic and pessimistic generalized variable precision rough set models based on maximal consistent blocks are constructed. The relationship between the two models and their main properties are analyzed. After that β-lower optimistic (pessimistic) and β-upper distribution attribute reduction is defined, and the corresponding judgement theorem is given. Boolean method of attribute reduction is obtained, and it can keep the upper (lower) approximation distribution of the decision class unchanged. This method of constructing discernibility set between maximal consistent blocks reduces the size of the discernibility matrix, and the process of computing attribute reduction is simplified, which can effectively save computing time and storage space. Then two examples of incomplete information systems with “lost” “don't care” values and only “don't care” values are employed to illustrate the proposed method. Finally, 5 sets of incomplete information data sets from UCI data set are used to validate its effectiveness. |
topic |
maximal consistent block variable precision rough set model attribute reduction incomplete information system |
url |
http://fcst.ceaj.org/CN/abstract/abstract2202.shtml |
work_keys_str_mv |
AT sunyanmijushengfengtaolileijunliangmeishe maximumconsistentblockbasedvariableprecisionroughsetmodelandattributereduction |
_version_ |
1721212967653998592 |