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.
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Acknowledgment
This research is supported by A*STAR, CISCO Systems (USA) Pte. Ltd, and National University of Singapore under its Cisco-NUS Accelerated Digital Economy Corporate Laboratory (Award I21001E0002) and the National Research Foundation Singapore under AI Singapore Programme (Award AISG-GC-2019-001-2B).
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Huang, L., Xing, Y., Denœux, T., Feng, M. (2024). An Evidential Time-to-Event Prediction Model Based on Gaussian Random Fuzzy Numbers. In: Bi, Y., Jousselme, AL., Denoeux, T. (eds) Belief Functions: Theory and Applications. BELIEF 2024. Lecture Notes in Computer Science(), vol 14909. Springer, Cham. https://doi.org/10.1007/978-3-031-67977-3_6
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