Survival analysis studies and predicts the time of death, or other singular
unrepeated events, based on historical data, while the true time of death for
some instances is unknown. Survival trees enable the discovery of complex
nonlinear relations in a compact human comprehensible model, by recursively
splitting the population and predicting a distinct survival distribution in
each leaf node. We use dynamic programming to provide the first survival tree
method with optimality guarantees, enabling the assessment of the optimality
gap of heuristics. We improve the scalability of our method through a special
algorithm for computing trees up to depth two. The experiments show that our
method's run time even outperforms some heuristics for realistic cases while
obtaining similar out-of-sample performance with the state-of-the-art.

基于历史数据，Survival Analysis 和 Survival Trees 使用动态规划提供了具有最优性保证的方法，发现复杂的非线性关系，并在实验中表现出良好的时间和性能。

最佳生存树：一种动态规划方法

Optimal Survival Trees: A Dynamic Programming Approach

We introduce the C++ application and R package ranger. The software is a fast
implementation of random forests for high dimensional data. Ensembles of
classification, regression and survival trees are supported. We describe the
implementation, provide examples, validate the package with a reference
implementation, and compare runtime and memory usage with other
implementations. The new software proves to scale best with the number of
features, samples, trees, and features tried for splitting. Finally, we show
that ranger is the fastest and most memory efficient implementation of random
forests to analyze data on the scale of a genome-wide association study.

介绍了一个快速实现高维数据随机森林的 C++ 应用和 R 软件包 ranger，支持分类、回归和生存树的集成。验证表明，该软件是用于分析基因组关联研究规模数据的最快速、最内存有效的随机森林实现。