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A case‐based reasoning approach for building a decision model
Author(s) -
Lee Jae Kwang,
Kim Jae Kyeong
Publication year - 2002
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/1468-0394.00198
Subject(s) - computer science , influence diagram , decision analysis , reuse , adaptive reasoning , case based reasoning , evidential reasoning approach , class (philosophy) , decision model , process (computing) , domain (mathematical analysis) , decision support system , adaptation (eye) , decision engineering , machine learning , management science , artificial intelligence , model based reasoning , decision tree , business decision mapping , knowledge representation and reasoning , mathematics , ecology , mathematical analysis , statistics , physics , biology , optics , economics , operating system
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.