Optimal survival trees
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10994_2021_Article_6117.pdf
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5.15 MB
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Unknown
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d403eaaf5df9b8ad203117b4bdd450dc
Author(s)
Bertsimas, Dimitris
Dunn, Jack
Gibson, Emma
Orfanoudaki, Agni
Date Issued
April 2022
Journal
Machine Learning
Publisher
Springer Science and Business Media LLC
Citation
Bertsimas, Dimitris, Dunn, Jack, Gibson, Emma and Orfanoudaki, Agni. 2022. "Optimal survival trees."
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10994-021-06117-0
