An Extension of the Evidential Neural Network Classifier based on Contextual Discounting

In this article, an extension of the Evidential Neural Network (ENN) introduced by Denœux is proposed by replacing the classical discounting operation with a refinement of the latter, called the contextual discounting (CD) operation, which allows for class-specific reliability degrees and yields a more expressive representation of uncertainty. This extension, named CD-ENN, is trained using the cross-entropy of the normalized contour function, making it efficient to learn its additional parameters. Experimental results on UCI datasets show that CD-ENN outperforms the original ENN in terms of predictive performance, as measured by the recently introduced generalized negative log-likelihood criterion.