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.

我们引入了一种基于证据的模型，用于预测具有被审查数据的事件发生时间。该模型使用高斯随机模糊数量来量化事件时间的不确定性，这是一种新引入的与信任函数相关的实数随机模糊子集族，可以推广高斯随机变量和高斯可能性分布。我们的方法对于底层事件发生时间分布做出了最小化的假设。通过最小化同时考虑正常和被审查数据的广义负对数似然函数，我们对模型进行了拟合。对两个真实世界数据集进行的比较实验证明了我们模型相对于现有技术的非常好的性能。