Conjunctive Rules in the Theory of Belief Functions and Their Justification Through Decisions Models

2016 ◽  
pp. 137-145 ◽  
Author(s):  
Andrey G. Bronevich ◽  
Igor N. Rozenberg
Keyword(s):  
Belief Functions ◽  
Theory Of Belief Functions

Belief Function Approach to Evidential Reasoning in Causal Maps

2005 ◽  
pp. 109-141
Author(s):  
Rajendra P. Srivastava ◽  
Mari W. Buche ◽  
Tom L. Roberts
Keyword(s):  
Job Satisfaction ◽  
Information Technology ◽  
Belief Function ◽  
Evidential Reasoning ◽  
Belief Functions ◽  
Causal Maps ◽  
Dempster Shafer Theory ◽  
Evidential Reasoning Approach ◽  
Theory Of Belief Functions ◽  
Shafer Theory

The purpose of this chapter is to demonstrate the use of the evidential reasoning approach under the Dempster-Shafer (D-S) theory of belief functions to analyze revealed causal maps (RCM). The participants from information technology (IT) organizations provided the concepts to describe the target phenomenon of Job Satisfaction. They also identified the associations between the concepts. This chapter discusses the steps necessary to transform a causal map into an evidential diagram. The evidential diagram can then be analyzed using belief functions technique with survey data, thereby extending the research from a discovery and explanation stage to testing and prediction. An example is provided to demonstrate these steps. This chapter also provides the basics of Dempster-Shafer theory of belief functions and a step-by-step description of the propagation process of beliefs in tree-like evidential diagrams.

A novel approach to pre-extracting support vectors based on the theory of belief functions

2016 ◽  
Vol 110 ◽  
pp. 210-223 ◽  
Author(s):  
Deqiang Han ◽  
Weibing Liu ◽  
Jean Dezert ◽  
Yi Yang
Keyword(s):  
Belief Functions ◽  
Support Vectors ◽  
Novel Approach ◽  
Theory Of Belief Functions

GENDIAG: A Computer-assisted Facility in Medical Genetics Based on Belief Functions

1985 ◽  
Vol 24 (04) ◽  
pp. 177-180 ◽  
Author(s):  
J. Gouvernet ◽  
M. Caraboeuf ◽  
S. Ayme
Keyword(s):  
Data Base ◽  
Relative Frequency ◽  
Medical Genetics ◽  
Computer Assisted ◽  
Belief Functions ◽  
Genetic Syndromes ◽  
Validation Phase ◽  
Theory Of Belief Functions ◽  
The Relationship

SummaryThe method is based on a diagnosis data base, each diagnosis being identified by a list of signs indexed by à rough estimation of their relative frequency. Signs are terms of a tree-structured thesaurus, the relationship child – father being equivalent to implication. Furthermore, any sign is weighted according to the possibility of it being a variant characteristic rather than a pathological one. The patient’s description being provided, the credibility of each diagnosis is computed according to Glenn Shafer’s theory of belief functions, and the most plausible diagnoses are proposed for the clinician’s choice. The results obtained during a validation phase were satisfying so that we have been routinely using the model for assistance in diagnosing uncommon genetic syndromes observed in our hospital or submitted by other centers.

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