scholarly journals Preference Elicitation with Uncertainty: Extending Regret Based Methods with Belief Functions

2019 ◽  
pp. 289-309
Author(s):  
Pierre-Louis Guillot ◽  
Sebastien Destercke
Keyword(s):  
Preference Elicitation ◽  
Belief Functions

Risk Preference Elicitation and the Role of Personality and Intelligence

2009 ◽  
Author(s):  
Tanja F. Blackstone ◽  
Jerry C. Crabb ◽  
Frederick L. Oswald
Keyword(s):  
Risk Preference ◽  
Preference Elicitation

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.

scholarly journals Preference elicitation for a ranking method based on multiple reference profiles

4OR ◽  
2021 ◽  
Author(s):  
Alexandru-Liviu Olteanu ◽  
Khaled Belahcene ◽  
Vincent Mousseau ◽  
Wassila Ouerdane ◽  
Antoine Rolland ◽  
...  
Keyword(s):  
Preference Elicitation ◽  
Ranking Method ◽  
Multiple Reference

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
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