The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence
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
Modeling vague beliefs using fuzzy-valued belief structures
Fuzzy Sets and Systems - Special issue on fuzzy numbers and uncertainty
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Multiple regression with fuzzy data
Fuzzy Sets and Systems
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Belief functions on real numbers
International Journal of Approximate Reasoning
Independence concepts in evidence theory
International Journal of Approximate Reasoning
Belief functions combination without the assumption of independence of the information sources
International Journal of Approximate Reasoning
Robust fuzzy regression analysis
Information Sciences: an International Journal
Maximum likelihood estimation from fuzzy data using the EM algorithm
Fuzzy Sets and Systems
Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework
IEEE Transactions on Knowledge and Data Engineering
Kernel based nonlinear fuzzy regression model
Engineering Applications of Artificial Intelligence
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In this paper, parametric regression analyses including both linear and nonlinear regressions are investigated in the case of imprecise and uncertain data, represented by a fuzzy belief function. The parameters in both the linear and nonlinear regression models are estimated using the fuzzy evidential EM algorithm, a straightforward fuzzy version of the evidential EM algorithm. The nonlinear regression model is derived by introducing a kernel function into the proposed linear regression model. An unreliable sensor experiment is designed to evaluate the performance of the proposed linear and nonlinear parametric regression methods, called parametric evidential regression (PEVREG) models. The experimental results demonstrate the high prediction accuracy of the PEVREG models in regressions with crisp inputs and a fuzzy belief function as output.