scholarly journals Idempotent Conjunctive Combination of Belief Functions by Distance Minimization

2016 ◽  
pp. 156-163 ◽  
Author(s):  
John Klein ◽  
Sebastien Destercke ◽  
Olivier Colot
Keyword(s):  
Belief Functions ◽  
Distance Minimization

scholarly journals Idempotent conjunctive and disjunctive combination of belief functions by distance minimization

2018 ◽  
Vol 92 ◽  
pp. 32-48 ◽  
Author(s):  
John Klein ◽  
Sebastien Destercke ◽  
Olivier Colot
Keyword(s):  
Belief Functions ◽  
Distance Minimization

Extended two-dimensional belief function based on divergence measurement

2020 ◽  
pp. 1-8
Author(s):  
Jianping Fan ◽  
Jing Wang ◽  
Meiqin Wu
Keyword(s):  
Belief Function ◽  
Distribution Functions ◽  
Divergence Measure ◽  
Uncertain Information ◽  
Belief Functions ◽  
Two Dimensional ◽  
Probability Distribution Functions ◽  
Basic Probability ◽  
The Difference ◽  
Supporting Degree

The two-dimensional belief function (TDBF = (mA, mB)) uses a pair of ordered basic probability distribution functions to describe and process uncertain information. Among them, mB includes support degree, non-support degree and reliability unmeasured degree of mA. So it is more abundant and reasonable than the traditional discount coefficient and expresses the evaluation value of experts. However, only considering that the expert’s assessment is single and one-sided, we also need to consider the influence between the belief function itself. The difference in belief function can measure the difference between two belief functions, based on which the supporting degree, non-supporting degree and unmeasured degree of reliability of the evidence are calculated. Based on the divergence measure of belief function, this paper proposes an extended two-dimensional belief function, which can solve some evidence conflict problems and is more objective and better solve a class of problems that TDBF cannot handle. Finally, numerical examples illustrate its effectiveness and rationality.

Econometric Forecasting Using Linear Regression and Belief Functions

2014 ◽  
pp. 304-312 ◽  
Author(s):  
Orakanya Kanjanatarakul ◽  
Philai Lertpongpiroon ◽  
Sombat Singkharat ◽  
Songsak Sriboonchitta
Keyword(s):  
Linear Regression ◽  
Belief Functions

Evaluation of Probability Transformations of Belief Functions for Decision Making

2016 ◽  
Vol 46 (1) ◽  
pp. 93-108 ◽  
Author(s):  
Deqiang Han ◽  
Jean Dezert ◽  
Zhansheng Duan
Keyword(s):  
Decision Making ◽  
Belief Functions

scholarly journals Features modeling with an α-stable distribution: Application to pattern recognition based on continuous belief functions

2013 ◽  
Vol 14 (4) ◽  
pp. 504-520 ◽  
Author(s):  
Anthony Fiche ◽  
Jean-Christophe Cexus ◽  
Arnaud Martin ◽  
Ali Khenchaf
Keyword(s):  
Pattern Recognition ◽  
Stable Distribution ◽  
Belief Functions

Construction of Belief Functions From Statistical Data About Reliability Under Epistemic Uncertainty

2013 ◽  
Vol 62 (3) ◽  
pp. 555-568 ◽  
Author(s):  
Felipe Aguirre ◽  
Mohamed Sallak ◽  
Walter Schon
Keyword(s):  
Statistical Data ◽  
Epistemic Uncertainty ◽  
Belief Functions

Cautious classification based on belief functions theory and imprecise relabelling

2022 ◽  
Vol 142 ◽  
pp. 130-146
Author(s):  
Abdelhak Imoussaten ◽  
Lucie Jacquin
Keyword(s):  
Belief Functions
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