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A hybrid knowledge‐based system applied to epidemic screening
Author(s) -
Li Lynn Ling X
Publication year - 1999
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 - 10.1111/1468-0394.00116
Subject(s) - computer science , case based reasoning , model based reasoning , expert system , artificial intelligence , reasoning system , rule based system , qualitative reasoning , focus (optics) , machine learning , knowledge based systems , opportunistic reasoning , knowledge representation and reasoning , physics , optics
Although many knowledge‐based systems (KBSs) focus on single‐paradigm approaches to encoding knowledge (such as production rules), experts rarely use a single type of knowledge in solving a problem. More often, an expert will apply a number of reasoning mechanisms. In recent years, rule‐based reasoning (RBR), case‐based reasoning (CBR) and model‐based reasoning (MBR) have emerged as important and complementary reasoning methodologies in artificial intelligence. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed for epidemic screening. The system has been tested using real data, and results are encouraging.