# A weighted random survival forest

arXiv: https://arxiv.org/abs/1901.00213 · HTML: https://mlsanity.com/paper/1901.00213/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2019.

## Built on by (most cited first, 6 of 6)

- [Attention-based Random Forest and Contamination Model](https://mlsanity.com/paper/2201.02880/) (2022)
- [Attention and Self-Attention in Random Forests](https://mlsanity.com/paper/2207.04293/) (2022)
- [Clustering Survival Data using a Mixture of Non-parametric Experts](https://mlsanity.com/paper/2405.15934/) (2024)
- [SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and Several Attention Mechanisms](https://mlsanity.com/paper/2412.07638/) (2024)
- [LARF: Two-level Attention-based Random Forests with a Mixture of Contamination Models](https://mlsanity.com/paper/2210.05168/) (2022)
- [Ensemble-Based Survival Models with the Self-Attended Beran Estimator Predictions](https://mlsanity.com/paper/2506.07933/) (2025)

## 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.