  We introduce an evidential model for time-to-event prediction with censored
data. In this model, uncertainty on event time is quantified by Gaussian random
fuzzy numbers, a newly introduced family of random fuzzy subsets of the real
line with associated belief functions, generalizing both Gaussian random
variables and Gaussian possibility distributions. Our approach makes minimal
assumptions about the underlying time-to-event distribution. The model is fit
by minimizing a generalized negative log-likelihood function that accounts for
both normal and censored data. Comparative experiments on two real-world
datasets demonstrate the very good performance of our model as compared to the
state-of-the-art.
