# A weighted random survival forest

## TL;DR

This paper introduces a weighted random survival forest that enhances hazard function estimation by optimizing tree weights through quadratic programming to maximize Harrell's C-index, leading to improved predictive performance.

## Contribution

It proposes a novel weighted approach for random survival forests, optimizing tree weights to improve hazard function estimation and predictive accuracy.

## Key findings

- Outperforms standard random survival forests on real data
- Optimizes tree weights via quadratic programming
- Achieves higher Harrell's C-index

## Abstract

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.