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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.13487 |
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| _version_ | 1866913398171631616 |
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| author | Huang, Ling Xing, Yucheng Denoeux, Thierry Feng, Mengling |
| author_facet | Huang, Ling Xing, Yucheng Denoeux, Thierry Feng, Mengling |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13487 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An evidential time-to-event prediction model based on Gaussian random fuzzy numbers Huang, Ling Xing, Yucheng Denoeux, Thierry Feng, Mengling Machine Learning 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. |
| title | An evidential time-to-event prediction model based on Gaussian random fuzzy numbers |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.13487 |