Learning and decision-making in the framework of fuzzy lattices
New learning paradigms in soft computing
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Bagging improves uncertainty representation in evidential pattern classification
Technologies for constructing intelligent systems
Ensembling evidential k-nearest neighbor classifiers through multi-modal perturbation
Applied Soft Computing
The combination of multiple classifiers using an evidential reasoning approach
Artificial Intelligence
Conditional Dempster-Shafer Theory for Uncertain Knowledge Updating
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Information Affinity: A New Similarity Measure for Possibilistic Uncertain Information
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A penalized likelihood based pattern classification algorithm
Pattern Recognition
Learning from data with uncertain labels by boosting credal classifiers
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
Reasoning with imprecise belief structures
International Journal of Approximate Reasoning
Representing uncertainty on set-valued variables using belief functions
Artificial Intelligence
Expert Systems with Applications: An International Journal
Imperfect pattern recognition using the fuzzy measure theory
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
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
Fusion of possibilistic sources of evidences for pattern recognition
Integrated Computer-Aided Engineering
International Journal of Approximate Reasoning
A comparison of dynamic and static belief rough set classifier
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
A k-nearest neighbours method based on lower previsions
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Rule discovery process based on rough sets under the belief function framework
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Evidence supporting measure of similarity for reducing the complexity in information fusion
Information Sciences: an International Journal
Computational Biology and Chemistry
Classification with dynamic reducts and belief functions
Transactions on rough sets XIV
Expert Systems with Applications: An International Journal
Classification systems based on rough sets under the belief function framework
International Journal of Approximate Reasoning
Distances in evidence theory: Comprehensive survey and generalizations
International Journal of Approximate Reasoning
Clustering approach using belief function theory
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
Determine discounting coefficient in data fusion based on fuzzy ART neural network
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Pattern classification using a penalized likelihood method
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
Partially supervised learning by a credal EM approach
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Information Sciences: an International Journal
Theory of evidence for face detection and tracking
International Journal of Approximate Reasoning
Computational Biology and Chemistry
A new belief-based K-nearest neighbor classification method
Pattern Recognition
A parts-based approach for automatic 3D shape categorization using belief functions
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
A proof for the positive definiteness of the Jaccard index matrix
International Journal of Approximate Reasoning
Random subspace evidence classifier
Neurocomputing
A belief function distance metric for orderable sets
Information Fusion
Generic discounting evaluation approach for urban image classification
IUKM'13 Proceedings of the 2013 international conference on Integrated Uncertainty in Knowledge Modelling and Decision Making
Evidential classifier for imprecise data based on belief functions
Knowledge-Based Systems
How to preserve the conflict as an alarm in the combination of belief functions?
Decision Support Systems
A belief classification rule for imprecise data
Applied Intelligence
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The paper presents a learning procedure for optimizing the parameters in the evidence-theoretic k-nearest neighbor rule, a pattern classification method based on the Dempster-Shafer theory of belief functions. In this approach, each neighbor of a pattern to be classified is considered as an item of evidence supporting certain hypotheses concerning the class membership of that pattern. Based on this evidence, basic belief masses are assigned to each subset of the set of classes. Such masses are obtained for each of the k-nearest neighbors of the pattern under consideration and aggregated using Dempster's rule of combination. In many situations, this method was found experimentally to yield lower error rates than other methods using the same information. However, the problem of tuning the parameters of the classification rule was so far unresolved. The authors determine optimal or near-optimal parameter values from the data by minimizing an error function. This refinement of the original method is shown experimentally to result in substantial improvement of classification accuracy