We propose a new classifier based on Dempster-Shafer (DS) theory and convolutional neural network (CNN) architecture for set-valued classification. In this classifier, called the evidential deep-learning pooling layers first extract high-dimensional features from input data. The are then converted into mass functions aggregated by Dempster’s rule in DS layer. Finally, an expected utility layer ...

Hidden Markov Fields (HMF) are widely applicable to various problems of image processing. In such models, the hidden process of interest X is a Markov field, which must be estimated from its observable noisy version Y. The success of HMF is due mainly to the fact that X remains Markov conditionally on the observed process, which facilitates different processing strategies such as Bayesian segme...

Dempster-Shafer evidence theory (D-S) is an effective instrument for merging the collected pieces of basic probability assignment (BPA), and it exhibits superiority in achieving robustness soft computing decision making uncertain imprecise environment. However, determination BPA still uncertain, merely applying can sometimes lead to counterintuitive results when lines conflict. In this paper, a...

We revisit Zadeh's notion of "evidence the second kind" and show that it provides foundation for a general theory epistemic random fuzzy sets, which generalizes both Dempster-Shafer belief functions possibility theory. In this perspective, deals with generated by while induced sets. The more allows us to represent combine evidence is uncertain fuzzy. demonstrate application formalism statistica...