Optimal Survival Trees

Dimitris Bertsimas, Jack Dunn, Emma Gibson, Agni Orfanoudaki

MLJ 2022 pp. 2951-3023

doi:10.1007/S10994-021-06117-0 /mlj/2022/bertsimas2022mlj-optimal/

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.

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Text

Bertsimas et al. "Optimal Survival Trees." Machine Learning, 2022. doi:10.1007/S10994-021-06117-0

Markdown

[Bertsimas et al. "Optimal Survival Trees." Machine Learning, 2022.](https://mlanthology.org/mlj/2022/bertsimas2022mlj-optimal/) doi:10.1007/S10994-021-06117-0

BibTeX

@article{bertsimas2022mlj-optimal,
  title     = {{Optimal Survival Trees}},
  author    = {Bertsimas, Dimitris and Dunn, Jack and Gibson, Emma and Orfanoudaki, Agni},
  journal   = {Machine Learning},
  year      = {2022},
  pages     = {2951-3023},
  doi       = {10.1007/S10994-021-06117-0},
  volume    = {111},
  url       = {https://mlanthology.org/mlj/2022/bertsimas2022mlj-optimal/}
}