한국지능정보시스템학회:학술대회논문집 (Proceedings of the Korea Inteligent Information System Society Conference)
- 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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- Pages.281-287
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- 1999
Neural Network-based Decision Class Analysis with Incomplete Information
초록
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 for 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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