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qualitative discovery in medical databasesImplication rules have been used in uncertainty reasoning systems to confirm and draw hypotheses or conclusions. However a major bottleneck in developing such systems lies in the elicitation of these rules. This paper empirically examines the performance of evidential inferencing with implication networks generated using a rule induction tool called KAT. KAT utilizes an algorithm for the statistical analysis of empirical case data, and hence reduces the knowledge engineering efforts and biases in subjective implication certainty assignment. The paper describes several experiments in which real-world diagnostic problems were investigated; namely, medical diagnostics. In particular, it attempts to show that: (1) with a limited number of case samples, KAT is capable of inducing implication networks useful for making evidential inferences based on partial observations, and (2) observation driven by a network entropy optimization mechanism is effective in reducing the uncertainty of predicted events.
Document ID
20000120142
Document Type
Preprint (Draft being sent to journal)
Authors
Maluf, David A.
(Research Inst. for Advanced Computer Science Moffett Field, CA United States)
Date Acquired
September 7, 2013
Publication Date
January 1, 2000
Subject Category
Documentation and Information Science
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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