DocumentCode :
2791579
Title :
Rough probability and rough expected value models
Author :
Fan, Yi-Jun ; Tian, Da-Zeng ; Ha, Ming-Hu
Author_Institution :
Coll. of Math. & Comput. Sci., Hebei Univ., Baoding
Volume :
4
fYear :
2008
fDate :
12-15 July 2008
Firstpage :
2357
Lastpage :
2362
Abstract :
Rough set theory is a new and effective mathematical theory used for processing incomplete, uncertain and vague data. In this paper, new set-theoretic operators - rough operators are proposed based on Pawlak rough set. On the basis of rough operators, a self-duality measure -rough probability is presented. On the rough probability space, rough variable, rough distribution, and rough expected value are introduced, their some properties are given. Finally, rough expected value models are established, and the rough expected value model of the convexity theorem is proved.
Keywords :
probability; rough set theory; rough distribution; rough expected value models; rough operators; rough probability; rough set theory; self-duality measure; set-theoretic operators; Cybernetics; Data analysis; Decision making; Educational institutions; Information systems; Machine learning; Mathematical model; Physics; Rough sets; Set theory; Rough expected value; Rough expected value model; Rough probability; Rough variable;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
Type :
conf
DOI :
10.1109/ICMLC.2008.4620799
Filename :
4620799
Link To Document :
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