Proceedings of the Korea Database Society Conference (한국데이타베이스학회:학술대회논문집)
- 1999.06a
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- Pages.281-287
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- 1999
Neural Network-based Decision Class Analysis with Incomplete Information
- Kim, Jae-Kyeong (Department of Management Information Systems, Kyonggi University) ;
- Lee, Jae-Kwang (Graduate School of Management, KAIS) ;
- Park, Kyung-Sam (Graduate School of Management, KAIST)
- Published : 1999.06.01
Abstract
Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data (a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology fur sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.
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