Efficient Decision Making Support System by Rough-Neural Network and x²


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 6, No. 8, pp. 2106-2112, Aug. 1999
10.3745/KIPSTE.1999.6.8.2106,   PDF Download:

Abstract

In decision-making, information is the thing manufactured as the useful type for decision-making. We can improve the efficiently of decision-making by elimination of unnecessary information. Rough set is the theory that can classify and reduce the unnecessary attributes. But the reduction process of rough set becomes more complex according to the number of attribute and tuple. After elimination of the dispensable attributes using x^2 and rough set, the indispensable attributes are used for the units of input layers in neural network. This rough-neural network can support mpre correct decision-making of neural network.


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Cite this article
[IEEE Style]
C. H. Mook, P. S. Young, C. K. Oak, "Efficient Decision Making Support System by Rough-Neural Network and x²," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 6, no. 8, pp. 2106-2112, 1999. DOI: 10.3745/KIPSTE.1999.6.8.2106.

[ACM Style]
Chung Hwan Mook, Pi Su Young, and Choi Kyoung Oak. 1999. Efficient Decision Making Support System by Rough-Neural Network and x². The Transactions of the Korea Information Processing Society (1994 ~ 2000), 6, 8, (1999), 2106-2112. DOI: 10.3745/KIPSTE.1999.6.8.2106.