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Raihan SMS, Ahmed M, Sharma A, Hossain MS, Islam RU, Andersson K. A Belief Rule Based Expert System to Diagnose Alzheimer’s Disease Using Whole Blood Gene Expression Data. Brain Inform 2022. [DOI: 10.1007/978-3-031-15037-1_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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Liu J, Yang J, Wang J, Sii H. Review of Uncertainty Reasoning Approaches as Guidance for Maritime and Offshore Safety-Based Assessment. ACTA ACUST UNITED AC 2016. [DOI: 10.1080/09617353.2002.11690751] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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The application of Dempster-Shafer theory demonstrated with justification provided by legal evidence. JUDGMENT AND DECISION MAKING 2007. [DOI: 10.1017/s1930297500000590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
AbstractIn forecasting and decision making, people can and often do represent a degree of belief in some proposition. At least two separate constructs capture such degrees of belief: likelihoods capturing evidential balance and support capturing evidential weight. This paper explores the weight or justification that evidence affords propositions, with subjects communicating using a belief function in hypothetical legal situations, where justification is a relevant goal. Subjects evaluated the impact of sets of 1–3 pieces of evidence, varying in complexity, within a hypothetical legal situation. The study demonstrates the potential usefulness of this evidential weight measure as an alternative or complement to the more-studied probability measure. Subjects’ responses indicated that weight and likelihood were distinguished; that subjects’ evidential weight tended toward single elements in a targeted fashion; and, that there were identifiable individual differences in reactions to conflicting evidence. Specifically, most subjects reacted to conflicting evidence that supported disjoint sets of suspects with continued support in the implicated sets, although an identifiable minority reacted by pulling back their support, expressing indecisiveness. Such individuals would likely require a greater amount of evidence than the others to counteract this tendency in support. Thus, the study identifies the value of understanding evidential weight as distinct from likelihood, informs our understanding of the psychology of individuals’ judgments of evidential weight, and furthers the application and meaningfulness of belief functions as a communication language.
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Jian-Bo Yang, Jun Liu, Jin Wang, How-Sing Sii, Hong-Wei Wang. Belief rule-base inference methodology using the evidential reasoning Approach-RIMER. ACTA ACUST UNITED AC 2006. [DOI: 10.1109/tsmca.2005.851270] [Citation(s) in RCA: 434] [Impact Index Per Article: 24.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Oriolo G, Ulivi G, Vendittelli M. Real-time map building and navigation for autonomous robots in unknown environments. ACTA ACUST UNITED AC 1998; 28:316-33. [DOI: 10.1109/3477.678626] [Citation(s) in RCA: 127] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Maglaras G, Nikolaidis E, Haftka RT, Cudney HH. Analytical-experimental comparison of probabilistic methods and fuzzy set based methods for designing under uncertainty. ACTA ACUST UNITED AC 1997. [DOI: 10.1007/bf01199225] [Citation(s) in RCA: 19] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Dongping Zhu, Conners R, Schmoldt D, Araman P. A prototype vision system for analyzing CT imagery of hardwood logs. ACTA ACUST UNITED AC 1996; 26:522-32. [DOI: 10.1109/3477.517028] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Petrovic D, Sweeney E. Fuzzy knowledge-based approach to treating uncertainty in inventory control. ACTA ACUST UNITED AC 1994. [DOI: 10.1016/0951-5240(94)90033-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Dohnal M, Vykydal J, Kvapilik M, Bures P. Practical uncertainty assessment of reasoning paths (fault trees) under total uncertainty ignorance. J Loss Prev Process Ind 1992. [DOI: 10.1016/0950-4230(92)80009-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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