An evidential time-to-event prediction model based on Gaussian random fuzzy numbers

TL;DR

This paper presents a novel evidential model for time-to-event prediction using Gaussian random fuzzy numbers, effectively handling censored data with minimal distributional assumptions.

Contribution

The introduction of Gaussian random fuzzy numbers for uncertainty quantification in time-to-event prediction is a novel approach that generalizes existing models.

Findings

Model achieves superior performance on real-world datasets

Handles censored data effectively with minimal assumptions

Outperforms state-of-the-art methods in experiments

Abstract

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.