DocumentCode :
424234
Title :
Variable precision rough set model based on general relation
Author :
Gong, Zeng-tai ; Sun, Bing-zhen ; Shao, Ya-Bin ; Chen, De-gang ; He, Qiang
Author_Institution :
Coll. of Math. & Inf. Sci., Northwest Normal Univ., Lanzhou, China
Volume :
4
fYear :
2004
fDate :
26-29 Aug. 2004
Firstpage :
2490
Abstract :
To make up for the drawbacks of the rough set model on general relation, a majority inclusion relation is defined and an error parameter α is introduced in order to give a variable precision rough set model based on general relation. In our rough set model, if α satisfies some conditions, then the model degenerates the basic rough set model which was first introduced by Z. Pawlak or degenerate the graded rough set model. What follows that model proposed in this paper is an extension of the classical basic rough set based on general relation and graded rough set model. After introducing the error parameter α, more useful information and data are collected and mined. Thus, the drawbacks, which lose more useful information for demanding the inclusion of the absolutely precision in the classical basic rough set model, are overcome.
Keywords :
rough set theory; error parameter; majority inclusion relation; variable precision rough set model; Data mining; Databases; Educational institutions; Electronic mail; Information science; Mathematical model; Mathematics; Set theory; Sun; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
Type :
conf
DOI :
10.1109/ICMLC.2004.1382222
Filename :
1382222
Link To Document :
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