DocumentCode :
552485
Title :
Novel algorithms of attribute reduction for variable precision rough set
Author :
Yang, Yan-yan ; Chen, De-gang ; Kwong, Sam
Author_Institution :
Dept. of Math. & Phys., North China Electr. Power Univ., Beijing, China
Volume :
1
fYear :
2011
fDate :
10-13 July 2011
Firstpage :
108
Lastpage :
112
Abstract :
The main application of variable precision rough set is to perform attribute reduction for databases. In variable precision rough set, the approach of discernibility matrix is theoretical foundation of finding reducts. In this paper, we observe that only minimal elements in the discernibility matrix is sufficient to find reducts, and every minimal element in the discernibility matrix is determined by one equivalence class pair relative to condition attributes at least; this fact motivates our idea in this paper to search the connection between this kind of pair and the minimal element in the discernibility matrix. By the connection between them, we develop the novel algorithms of finding reducts, which improve the existing ones in terms of discernibility matrix.
Keywords :
database theory; matrix algebra; rough set theory; attribute reduction; discernibility matrix; equivalence class pair; variable precision rough set; Algorithm design and analysis; Analytical models; Brain modeling; Computational modeling; Cybernetics; Finite element methods; Machine learning; Discernibility matrix; Equivalence class pair relative to condition attributes; Minimal element; Variable precision rough set;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2011 International Conference on
Conference_Location :
Guilin
ISSN :
2160-133X
Print_ISBN :
978-1-4577-0305-8
Type :
conf
DOI :
10.1109/ICMLC.2011.6016740
Filename :
6016740
Link To Document :
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