A view on conditional measures through local representability of binary relations
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Pattern Recognition and Information Fusion Using Belief Functions: Some Recent Developments
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Consonant Belief Function Induced by a Confidence Set of Pignistic Probabilities
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Decision fusion for postal address recognition using belief functions
Expert Systems with Applications: An International Journal
Extending stochastic ordering to belief functions on the real line
Information Sciences: an International Journal
Multi-camera people tracking using evidential filters
International Journal of Approximate Reasoning
Combination of partially non-distinct beliefs: The cautious-adaptive rule
International Journal of Approximate Reasoning
RECM: Relational evidential c-means algorithm
Pattern Recognition Letters
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
An Evidence-Theoretic k-Nearest Neighbor Rule for Multi-label Classification
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
International Journal of Approximate Reasoning
Mass function derivation and combination in multivariate data spaces
Information Sciences: an International Journal
Hierarchical and conditional combination of belief functions induced by visual tracking
International Journal of Approximate Reasoning
Shape from silhouette using Dempster-Shafer theory
Pattern Recognition
Representing uncertainty on set-valued variables using belief functions
Artificial Intelligence
International Journal of Approximate Reasoning
Evidential multi-label classification approach to learning from data with imprecise labels
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
A belief function classifier based on information provided by noisy and dependent features
International Journal of Approximate Reasoning
Application of fuzzy TOPSIS in evaluating sustainable transportation systems
Expert Systems with Applications: An International Journal
Particle filtering in the Dempster--Shafer theory
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Pain monitoring: A dynamic and context-sensitive system
Pattern Recognition
Non-exclusive hypotheses in Dempster--Shafer Theory
International Journal of Approximate Reasoning
A skin detection approach based on the Dempster--Shafer theory of evidence
International Journal of Approximate Reasoning
Theory of evidence for face detection and tracking
International Journal of Approximate Reasoning
A new belief-based K-nearest neighbor classification method
Pattern Recognition
Robust kernelized approach to clustering by incorporating new distance measure
Engineering Applications of Artificial Intelligence
International Journal of Approximate Reasoning
Evidential classifier for imprecise data based on belief functions
Knowledge-Based Systems
A new decision-making method by incomplete preferences based on evidence distance
Knowledge-Based Systems
A belief classification rule for imprecise data
Applied Intelligence
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The transferable belief model (TBM) is a model to represent quantified uncertainties based on belief functions, unrelated to any underlying probability model. In this framework, two main approaches to pattern classification have been developed: the TBM model-based classifier, relying on the general Bayesian theorem (GBT), and the TBM case-based classifier, built on the concept of similarity of a pattern to be classified with training patterns. Until now, these two methods seemed unrelated, and their connection with standard classification methods was unclear. This paper shows that both methods actually proceed from the same underlying principle, i.e., the GBT, and that they essentially differ by the nature of the assumed available information. This paper also shows that both methods collapse to a kernel rule in the case of precise and categorical learning data and for certain initial assumptions, and a simple relationship between basic belief assignments produced by the two methods is exhibited in a special case. These results shed new light on the issues of classification and supervised learning in the TBM. They also suggest new research directions and may help users in selecting the most appropriate method for each particular application, depending on the nature of the information at hand