The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering interval-valued proximity data using belief functions
Pattern Recognition Letters
ECM: An evidential version of the fuzzy c-means algorithm
Pattern Recognition
Ensemble clustering in the belief functions framework
International Journal of Approximate Reasoning
Classifier fusion in the Dempster--Shafer framework using optimized t-norm based combination rules
International Journal of Approximate Reasoning
Belief C-Means: An extension of Fuzzy C-Means algorithm in belief functions framework
Pattern Recognition Letters
CECM: Constrained evidential C-means algorithm
Computational Statistics & Data Analysis
An evidence-theoretic k-NN rule with parameter optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
EVCLUS: evidential clustering of proximity data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
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The K-nearest neighbor (K-NN) classification method originally developed in the probabilistic framework has serious difficulties to classify correctly the close data points (objects) originating from different classes. To cope with such difficult problem and make the classification result more robust to misclassification errors, we propose a new belief-based K-nearest neighbor (BK-NN) method that allows each object to belong both to the specific classes and to the sets of classes with different masses of belief. BK-NN is able to provide a hyper-credal classification on the specific classes, the rejection classes and the meta-classes as well. Thus, the objects hard to classify correctly are automatically committed to a meta-class or to a rejection class, which can reduce the misclassification errors. The basic belief assignment (bba) of each object is defined from the distance between the object and its neighbors and from the acceptance and rejection thresholds. The bba's are combined using a new combination method specially developed for the BK-NN. Several experiments based on simulated and real data sets have been carried out to evaluate the performances of the BK-NN method with respect to several classical K-NN approaches.