Approaches to decision-making under uncertainty in the belief function framework are
reviewed. Most methods are shown to blend criteria for decision under ignorance with the …

In decision-making systems, how to address uncertainty plays an important role for the
improvement of system performance in uncertainty reasoning. Dempster–Shafer evidence …

Complex evidence theory (CET) is an effective method for uncertainty reasoning in
knowledge-based systems with good interpretability that has recently attracted much …

We propose a new classifier based on Dempster-Shafer (DS) theory and a convolutional
neural network (CNN) architecture for set-valued classification. In this classifier, called the …

Statistical prediction models have gained popularity in applied research. One challenge is
the transfer of the prediction model to a different population which may be structurally …

We revisit logistic regression and its nonlinear extensions, including multilayer feedforward
neural networks, by showing that these classifiers can be viewed as converting input or …

In machine learning, some models can make uncertain and imprecise predictions, they are
called evidential models. These models may also be able to handle imperfect labeling and …

Many applications using data mining and machine learning techniques require posterior
probability estimates besides often highly accurate predictions. Classifier calibration is a …

40 years of Dempster-Shafer theory | International Journal of Approximate Reasoning skip to
main content ACM Digital Library home ACM home Google, Inc. (search) Advanced Search …

This study proposes an architecture based on a weighted fuzzy Dempster-Shafer framework
(WFDSF), which can adjust weights associated with inconsistent evidence obtained by …