Refined modeling of sensor reliability in the belief function framework using contextual discounting

Authors:
David Mercier;Benjamin Quost;Thierry Denux
Affiliations:
UMR CNRS 6599, Heudiasyc, Université de Technologie de Compiègne, BP 20529, F-60205 Compiègne Cedex, France and SOLYSTIC, 14 avenue Raspail, F-94257 Gentilly Cedex, France;UMR CNRS 6599, Heudiasyc, Université de Technologie de Compiègne, BP 20529, F-60205 Compiègne Cedex, France;UMR CNRS 6599, Heudiasyc, Université de Technologie de Compiègne, BP 20529, F-60205 Compiègne Cedex, France
Venue:
Information Fusion
Year:
2008

Citing 7
Cited 24

The nature of the unnormalized beliefs encountered in the transferable belief model

UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
The transferable belief model

Artificial Intelligence
What is Dempster-Shafer's model?

Advances in the Dempster-Shafer theory of evidence
Contextual discounting of belief functions

ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
An evidence-theoretic k-NN rule with parameter optimization

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Sensor fusion in anti-personnel mine detection using a two-level belief function model

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Assessing sensor reliability for multisensor data fusion within the transferable belief model

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Constructing consonant belief functions from sample data using confidence sets of pignistic probabilities

International Journal of Approximate Reasoning
On Latent Belief Structures

ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Forward-Backward-Viterbi Procedures in the Transferable Belief Model for State Sequence Analysis Using Belief Functions

ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Robust combination rules for evidence theory

Information Fusion
Combination of partially non-distinct beliefs: The cautious-adaptive rule

International Journal of Approximate Reasoning
Learning from data with uncertain labels by boosting credal classifiers

Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
Mass function derivation and combination in multivariate data spaces

Information Sciences: an International Journal
Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion

Engineering Applications of Artificial Intelligence
Hierarchical and conditional combination of belief functions induced by visual tracking

International Journal of Approximate Reasoning
Conflict management in Dempster--Shafer theory using the degree of falsity

International Journal of Approximate Reasoning
Classifier fusion in the Dempster--Shafer framework using optimized t-norm based combination rules

International Journal of Approximate Reasoning
Object association with belief functions, an application with vehicles

Information Sciences: an International Journal
Combining binary classifiers with imprecise probabilities

IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
The combination of dependence-based interval-valued evidential reasoning approach with balanced scorecard for performance assessment

Expert Systems with Applications: An International Journal
Evidential calibration process of multi-agent based system: An application to forensic entomology

Expert Systems with Applications: An International Journal
Singular sources mining using evidential conflict analysis

International Journal of Approximate Reasoning
Pain monitoring: A dynamic and context-sensitive system

Pattern Recognition
Distances in evidence theory: Comprehensive survey and generalizations

International Journal of Approximate Reasoning
Belief functions contextual discounting and canonical decompositions

International Journal of Approximate Reasoning
Relevance and truthfulness in information correction and fusion

International Journal of Approximate Reasoning
The conjunctive combination of interval-valued belief structures from dependent sources

International Journal of Approximate Reasoning
Selecting source behavior in information fusion on the basis of consistency and specificity

ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Conjunctive combination of belief functions from dependent sources using positive and negative weight functions

Expert Systems with Applications: An International Journal
Controlling Remanence in Evidential Grids Using Geodata for Dynamic Scene Perception

International Journal of Approximate Reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

In belief functions theory, the discounting operation allows to combine information provided by a source in the form of a belief function with meta-knowledge regarding the reliability of that source, resulting in a ''weakened'', less informative belief function. In this article, an extension of the discounting operation is proposed, allowing to use more detailed information regarding the reliability of the source in different contexts, i.e., conditionally on different hypotheses regarding the variable on interest. This results in a contextual discounting operation parameterized with a discount rate vector. Some properties of this contextual discounting operation are studied, and its relationship with classical discounting is explained. A method for learning the discount rates is also presented.