The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Machine Learning
A K-NN Associated Fuzzy Evidential Reasoning Classifier with Adaptive Neighbor Selection
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Ensembling evidential k-nearest neighbor classifiers through multi-modal perturbation
Applied Soft Computing
ECM: An evidential version of the fuzzy c-means algorithm
Pattern Recognition
Belief C-Means: An extension of Fuzzy C-Means algorithm in belief functions framework
Pattern Recognition Letters
A K-nearest neighbours method based on imprecise probabilities
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Knowledge Extraction from Low Quality Data: Theoretical, Methodological and Practical Issues
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
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Variational Gaussian process classifiers
IEEE Transactions on Neural Networks
Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework
IEEE Transactions on Knowledge and Data Engineering
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A new evidential classifier (EC) based on belief functions is developed in this paper for the classification of imprecise data using K-nearest neighbors. EC works with credal classification which allows to classify the objects either in the specific classes, in the meta-classes defined by the union of several specific classes, or in the ignorant class for the outlier detection. The main idea of EC is to not classify an object in a particular class whenever the object is simultaneously close to several classes that turn to be indistinguishable for it. In such case, EC will associate the object with a proper meta-class in order to reduce the misclassification errors. The full ignorant class is interpreted as the class of outliers representing all the objects that are too far from the other data. The K basic belief assignments (bba's) associated with the object are determined by the distances of the object to its K-nearest neighbors and some chosen imprecision thresholds. The classification of the object depends on the global combination results of these K bba's. The interest and potential of this new evidential classifier with respect to other classical methods are illustrated through several examples based on artificial and real data sets.