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