Neural network-based decision class analysis for building topological-level influence diagram

Kim, Jae Kyeong; Park, Kyung Sam

doi:10.1006/ijhc.1997.0144

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…To implement DCA, domainspecific knowledge of a predefined decision class is stored at an (explicit-typed or implicit-typed) knowledge base and it is retrieved to model a new decision problem. As a methodology to implement DCA, rule-based approaches (Reed, 1989;Kim, 1991;Chung et al, 1992), frame-based approaches (Sonnenberg et al, 1994) and neural-networkbased approaches (Kim & Park, 1997;Kim & Chu, 1998) have been used until now. Our previous research (Kim & Park, 1997) indicates that the use of neural networks to generate IDs at the topological level results in good performance, but the generated ID is usually not a complete model, called a well-formed ID.…”

Section: Introductionmentioning

confidence: 99%

A case‐based reasoning approach for building a decision model

Lee

Kim

2002

Expert Systems

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A methodology based on case-based reasoning is proposed to build a topological-level influence diagram. It is then applied to a project proposal review process. The formulation of decision problems requires much time and effort, and the resulting model, such as an influence diagram, is applicable only to one specific problem. However, some prior knowledge from the experience in modeling influence diagrams can be utilized to resolve other similar decision problems. The basic idea of case-based reasoning is that humans reuse the problem-solving experience to solve new problems.In this paper, we suggest case-based decision class analysis (CB-DCA), a methodology based on case-based reasoning, to build an influence diagram. CB-DCA is composed of a case retrieval procedure and an adaptation procedure. Two measures are suggested for the retrieval procedure, one a fitting ratio and the other a garbage ratio. The adaptation procedure is based on decision-analytic knowledge and decision participants' domain-specific knowledge. Our proposed methodology has been applied to an environmental review process in which decision-makers need decision models to decide whether a project proposal is accepted or not. Experimental results show that our methodology for decision class analysis provides decision-makers with robust knowledge-based support.

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