A weighted random survival forest
Not on the map. Why?
Abstract (arXiv)
A weighted random survival forest is presented in the paper. It can be regarded as a modification of the random forest improving its performance. The main idea underlying the proposed model is to replace the standard procedure of averaging used for estimation of the random survival forest hazard function by weighted avaraging where the weights are assigned to every tree and can be veiwed as training paremeters which are computed in an optimal way by solving a standard quadratic optimization problem maximizing Harrell's C-index. Numerical examples with real data illustrate the outperformance of the proposed model in comparison with the original random survival forest.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Jan 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 3; 2023: 0; 2024: 2; 2025: 1.
6 papers.
- 2022Attention-based Random Forest and Contamination Modelcited by 10
- 2022Attention and Self-Attention in Random Forestscited by 4
- 2024Clustering Survival Data using a Mixture of Non-parametric Expertscited by 3
- 2024SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and Several Attention Mechanismscited by 1
- 2022LARF: Two-level Attention-based Random Forests with a Mixture of Contamination Modelscited by 0
- 2025Ensemble-Based Survival Models with the Self-Attended Beran Estimator Predictionscited by 0
- 2025Ensemble-Based Survival Models with the Self-Attended Beran Estimator Predictions9 Jun 2025
- 2024SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and Several Attention Mechanisms10 Dec 2024
- 2024Clustering Survival Data using a Mixture of Non-parametric Experts24 May 2024
- 2022LARF: Two-level Attention-based Random Forests with a Mixture of Contamination Models11 Oct 2022
- 2022Attention and Self-Attention in Random Forests9 Jul 2022
- 2022Attention-based Random Forest and Contamination Model8 Jan 2022