Summary of An Evidential Time-to-event Prediction Model Based on Gaussian Random Fuzzy Numbers, by Ling Huang et al.
An evidential time-to-event prediction model based on Gaussian random fuzzy numbers
by Ling Huang, Yucheng Xing, Thierry Denoeux, Mengling Feng
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces an evidential model for time-to-event prediction with censored data, which quantifies uncertainty using Gaussian random fuzzy numbers. The model makes minimal assumptions about the underlying distribution and is fit by minimizing a generalized negative log-likelihood function that accounts for both normal and censored data. Compared to state-of-the-art methods on two real-world datasets, our model performs very well. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to predict when something will happen in the future, even if we don’t know exactly when it will happen. They use special numbers called “Gaussian random fuzzy numbers” that can show how uncertain we are about when something might happen. This approach is flexible and doesn’t make strong assumptions about what’s happening. The model does well on real-world examples compared to other methods. |
Keywords
* Artificial intelligence * Log likelihood
