Analyzing a class of decision problems: Neural network based approach

Jae Kyeong Kim, Seok Chin Chu

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

This paper presents an application of an artificial neural net to the implementation of decision class analysis (DCA), together with the generation of a decision model, influence diagram. The diagram is well-known as a good tool for knowledge representation of complex decision problems. Generating influence diagram is known to in practice require much time and effort, and the resulting model can be generally applicable to only a specific decision problem. In order to reduce the burden of modeling decision problems, the concept of DCA is introduced. DCA under consideration is viewed as a classification problem where a set of input-output data pairs is given. We thus propose a method utilizing a feedforward neural net with supervised learning rule to develop DCA based on influence diagram. We also examine the results of neural net simulation with an example of a class of decision problems.

Original languageEnglish
Pages (from-to)36-43
Number of pages8
JournalProceedings of the Hawaii International Conference on System Sciences
Volume5
Publication statusPublished - 1998
EventProceedings of the 1998 31st Annual Hawaii International Conference on System Sciences. Part 1 (of 7) - Big Island, HI, USA
Duration: 6 Jan 19989 Jan 1998

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