Artificial Intelligence
What is Dempster-Shafer's model?
Advances in the Dempster-Shafer theory of evidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Hybrid Approach to Handwritten Address Verification
International Journal of Computer Vision
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
An evidential cooperative multi-agent system
Expert Systems with Applications: An International Journal
Decision making in the TBM: the necessity of the pignistic transformation
International Journal of Approximate Reasoning
Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Representing partial ignorance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Using Logic to Understand Relations between DSmT and Dempster-Shafer Theory
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Expert Systems with Applications: An International Journal
Maximal confidence intervals of the interval-valued belief structure and applications
Information Sciences: an International Journal
Towards an alarm for opposition conflict in a conjunctive combination of belief functions
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Object association with belief functions, an application with vehicles
Information Sciences: an International Journal
Evidential calibration process of multi-agent based system: An application to forensic entomology
Expert Systems with Applications: An International Journal
Belief functions contextual discounting and canonical decompositions
International Journal of Approximate Reasoning
Non-exclusive hypotheses in Dempster--Shafer Theory
International Journal of Approximate Reasoning
Decision fusion of horizontal and vertical trajectories for recognition of online Farsi subwords
Engineering Applications of Artificial Intelligence
How to preserve the conflict as an alarm in the combination of belief functions?
Decision Support Systems
| Hi-index | 12.05 |
Combining the outputs from several postal address readers (PARs) is a promising approach for improving the performances of mailing address recognition systems. In this paper, this problem is solved using the Transferable Belief Model, an uncertain reasoning framework based on Dempster-Shafer belief functions. Applying this framework to postal address recognition implies defining the frame of discernment (or set of possible answers to the problem under study), converting PAR outputs into belief functions (taking into account additional information such as confidence scores when available), combining the resulting belief functions, and making decisions. All these steps are detailed in this paper. Experimental results demonstrate the effectiveness of this approach as compared to simple combination rules.