DocumentCode :
389626
Title :
Rough neural classifier system
Author :
Hassan, Yasser ; Tazaki, Eiichiro ; Egawa, Shin ; Suyama, Kazuho
Author_Institution :
Dept. of Control & Syst. Eng., Toin Univ. of Yokohama, Japan
Volume :
5
fYear :
2002
fDate :
6-9 Oct. 2002
Abstract :
The methodology for using rough set theory for preference modeling in a decision problem is presented in which we will introduce a new method where a neural network system and rough set theory are completely integrated into a hybrid system and used cooperatively for decision and classification support. At the first glance, the two methods we talk about have not too much in common. But, in spite of the differences between these two methods, it is interesting to try to incorporate both into one combined system, and apply it in the building of a decision support system.
Keywords :
classification; data mining; decision support systems; neural nets; rough set theory; very large databases; classification; database knowledge discovery; decision problem; decision support system; hybrid system; neural network system; preference modeling; rough neural classifier system; rough set theory; Artificial neural networks; Biological neural networks; Control systems; Databases; Electronic mail; Medical control systems; Neurons; Rough sets; Systems engineering and theory; Transfer functions;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2002 IEEE International Conference on
ISSN :
1062-922X
Print_ISBN :
0-7803-7437-1
Type :
conf
DOI :
10.1109/ICSMC.2002.1176404
Filename :
1176404
Link To Document :
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