Optimal Survival Trees

TL;DR

This paper introduces an Optimal Survival Trees algorithm that uses mixed-integer optimization and local search to produce globally optimized survival trees, improving accuracy especially in large datasets.

Contribution

The paper presents a novel OST algorithm that enhances survival tree modeling by integrating MIO and local search for global optimality.

Findings

OST outperforms existing survival tree methods in accuracy

Improved performance on large datasets

Global optimization leads to better model quality

Abstract

Tree-based models are increasingly popular due to their ability to identify complex relationships that are beyond the scope of parametric models. Survival tree methods adapt these models to allow for the analysis of censored outcomes, which often appear in medical data. We present a new Optimal Survival Trees algorithm that leverages mixed-integer optimization (MIO) and local search techniques to generate globally optimized survival tree models. We demonstrate that the OST algorithm improves on the accuracy of existing survival tree methods, particularly in large datasets.