Summary of Survbeta: Ensemble-based Survival Models Using Beran Estimators and Several Attention Mechanisms, by Lev V. Utkin et al.
SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and Several Attention Mechanisms
by Lev V. Utkin, Semen P. Khomets, Vlada A. Efremenko, Andrei V. Konstantinov
First submitted to arxiv on: 10 Dec 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 The SurvBETA model, an ensemble-based approach for machine learning in survival analysis, is introduced. This novel method combines the Beran estimator as a weak learner with attention mechanisms to create conditional survival functions. The Beran estimator is used three times: once to implement the model, again to determine prototypes of bootstrap samples, and finally to aggregate predictions. SurvBETA can be trained in either a general or simplified form, depending on the optimization problem solved. Numerical experiments demonstrate its performance on synthetic data and compare it to other survival models on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SurvBETA is a new machine learning model that helps predict how long something will last, like how long someone with a certain disease will live. It uses special math called ensemble-based methods and attention mechanisms to make predictions. This model can be used in different ways, depending on the problem it’s trying to solve. Researchers tested this model on fake data and real-world data from hospitals, and compared its results to other models. The code for SurvBETA is publicly available so that others can use and improve it. |
Keywords
» Artificial intelligence » Attention » Machine learning » Optimization » Synthetic data
