  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 evidential deep-learning classifier,
convolutional and pooling layers first extract high-dimensional features from
input data. The features are then converted into mass functions and aggregated
by Dempster's rule in a DS layer. Finally, an expected utility layer performs
set-valued classification based on mass functions. We propose an end-to-end
learning strategy for jointly updating the network parameters. Additionally, an
approach for selecting partial multi-class acts is proposed. Experiments on
image recognition, signal processing, and semantic-relationship classification
tasks demonstrate that the proposed combination of deep CNN, DS layer, and
expected utility layer makes it possible to improve classification accuracy and
to make cautious decisions by assigning confusing patterns to multi-class sets.
