The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence
Constructing the Pignistic Probability Function in a Context of Uncertainty
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
An Adaptive k-NN Rule Based on Dempster-Shafer Theory
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
On optimum recognition error and reject tradeoff
IEEE Transactions on Information Theory
Handling Different Forms of Uncertainty in Regression Analysis: A Fuzzy Belief Structure Approach
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Bagging improves uncertainty representation in evidential pattern classification
Technologies for constructing intelligent systems
On the combination and normalization of interval-valued belief structures
Information Sciences: an International Journal
Multisensor triplet Markov chains and theory of evidence
International Journal of Approximate Reasoning
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Expert Systems with Applications: An International Journal
Reasoning with imprecise belief structures
International Journal of Approximate Reasoning
Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Learning by discovering conflicts
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
A robust adaptive version of evidence-theoretic k-NN classification rule
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 4
International Journal of Approximate Reasoning
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
On the dynamic evidential reasoning algorithm for fault prediction
Expert Systems with Applications: An International Journal
Classifier fusion in the Dempster--Shafer framework using optimized t-norm based combination rules
International Journal of Approximate Reasoning
Maximal confidence intervals of the interval-valued belief structure and applications
Information Sciences: an International Journal
Object association with belief functions, an application with vehicles
Information Sciences: an International Journal
A skin detection approach based on the Dempster--Shafer theory of evidence
International Journal of Approximate Reasoning
Application of decision-making techniques in supplier selection: A systematic review of literature
Expert Systems with Applications: An International Journal
Random subspace evidence classifier
Neurocomputing
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The Dempster-Shafer theory provides a convenient framework for decision making based on very limited or weak information. Such situations typically arise in pattern recognition problems when patterns have to be classified based on a small number of training vectors, or when the training set does not contain samples from all classes. This paper examines different strategies that can be applied in this context to reach a decision (e.g. assignment to a class or rejection), provided the possible consequences of each action can be quantified. The corresponding decision rules are analysed under different assumptions concerning the completeness of the training set. These approaches are then demonstrated using real data.