Bayesian, Evidence, Fuzzy: Which Theory Works Best When Reasoning with Uncertain Knowledge?,
James A. Shine · Defense Technical Information Center (DTIC) · 1985
The research group of which the author is a member has been studying various techniques of automated digital image feature extraction for several years. One increasingly thorny problem has been how to make decisions when the available information is incomplete or uncertain. Classical Bayesian probability theory has been used in the past, with limited success at best. Within the last year or so, we have been exploring alternatives to Bayesian probability, with emphasis on two in particular: Shafer's theory of evidence, and Zadeh's fuzzy logic. We feel that all three theories have something to offer to help solve the feature extraction problem, and we are currently looking toward combining aspects of all three theories under one roof. Keywords include: Automated Feature Extraction, Fuzzy Logic, Evidence Theory, Bayesian Decision Theory, Knowledge-based information.