The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Clustering interval-valued proximity data using belief functions
Pattern Recognition Letters
ECM: An evidential version of the fuzzy c-means algorithm
Pattern Recognition
Fusion of imprecise qualitative information
Applied Intelligence
Hybrid ensemble approach for classification
Applied Intelligence
Belief C-Means: An extension of Fuzzy C-Means algorithm in belief functions framework
Pattern Recognition Letters
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
A neural network classifier based on Dempster-Shafer theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Measuring ambiguity in the evidence theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Remarks on “Measuring Ambiguity in the Evidence Theory”
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Simultaneous optimization of artificial neural networks for financial forecasting
Applied Intelligence
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The classification of imprecise data is a difficult task in general because the different classes can partially overlap. Moreover, the available attributes used for the classification are often insufficient to make a precise discrimination of the objects in the overlapping zones. A credal partition (classification) based on belief functions has already been proposed in the literature for data clustering. It allows the objects to belong (with different masses of belief) not only to the specific classes, but also to the sets of classes called meta-classes which correspond to the disjunction of several specific classes. In this paper, we propose a new belief classification rule (BCR) for the credal classification of uncertain and imprecise data. This new BCR approach reduces the misclassification errors of the objects difficult to classify by the conventional methods thanks to the introduction of the meta-classes. The objects too far from the others are considered as outliers. The basic belief assignment (bba) of an object is computed from the Mahalanobis distance between the object and the center of each specific class. The credal classification of the object is finally obtained by the combination of these bba's associated with the different classes. This approach offers a relatively low computational burden. Several experiments using both artificial and real data sets are presented at the end of this paper to evaluate and compare the performances of this BCR method with respect to other classification methods.