DocumentCode
1937691
Title
Information Granules and Approximations in Incomplete Information Systems
Author
Wu, Wei-Zhi ; Yang, Xiao-Ping
Author_Institution
Zhejiang Ocean Univ., Zhoushan
Volume
7
fYear
2007
fDate
19-22 Aug. 2007
Firstpage
3740
Lastpage
3745
Abstract
In this paper three types of information granular structures, called similarity classes, maximal consistent blocks, and labeled blocks, in incomplete information systems are introduced. Their properties are examined. Based on the three structures of granules, three types of rough set approximation models are derived for mining of certain and possible rules in incomplete decision tables. The relationships among the three rough set models are established.
Keywords
decision tables; information theory; rough set theory; granular structures; incomplete decision tables; incomplete information systems; information granules; labeled blocks; maximal consistent blocks; rough set approximation model; similarity classes; Computational Intelligence Society; Cybernetics; Information science; Information systems; Machine learning; Mathematics; Oceans; Physics; Rough sets; Uncertainty; Approximations; Granular computing; Granules; Incomplete information systems; Labeled block sets; Rough sets;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-0973-0
Electronic_ISBN
978-1-4244-0973-0
Type
conf
DOI
10.1109/ICMLC.2007.4370798
Filename
4370798
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