Artificial Intelligence
Advances in the Dempster-Shafer theory of evidence
Advances in the Dempster-Shafer theory of evidence
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Fuzzy model identification: selected approaches
Fuzzy model identification: selected approaches
Fuzzy sets in decision analysis, operations research and statistics
A generalization of the dempster-shafer theory
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Analysis of evidence-theoretic decision rules for pattern classification
Pattern Recognition
Approximations for decision making in the Dempster-Shafer theory of evidence
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Coarsening Approximations of Belief Functions
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Information-based dissimilarity assessment in Dempster-Shafer theory
Knowledge-Based Systems
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We propose a new approach to functional regression based on fuzzy evidence theory. This method uses a training set for computing a fuzzy belief structure which quantifies different types of uncertainties, such as nonspecificity, conflict, or low density of input data. The method can cope with a very large class of training data, such as numbers, intervals, fuzzy numbers, and, more generally, fuzzy belief structures. In order to limit calculations and improve output readability, we propose a belief structure simplification method, based on similarity between fuzzy sets and significance of these sets. The proposed model can provide predictions in several different forms, such as numerical, probabilistic, fuzzy or as a fuzzy belief structure. To validate the model, we propose two simulations and compare the results with classical or fuzzy regression methods.