An evidential time-to-event prediction model based on Gaussian random fuzzy numbers - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

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

Résumé

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.
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Dates et versions

hal-04674357 , version 1 (21-08-2024)

Identifiants

Citer

L. Huang, Yucheng Xing, Thierry Denoeux, Mengling Feng. An evidential time-to-event prediction model based on Gaussian random fuzzy numbers. 8th International Conference on Belief Functions (BELIEF 2024), Sep 2024, Belfast, United Kingdom. ⟨10.1007/978-3-031-67977-3_6⟩. ⟨hal-04674357⟩
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