Optimal Sparse Survival Trees

Rui Zhang, Rui Xin, Margo Seltzer, Cynthia Rudin
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:352-360, 2024.

Abstract

Interpretability is crucial for doctors, hospitals, pharmaceutical companies and biotechnology corporations to analyze and make decisions for high stakes problems that involve human health. Tree-based methods have been widely adopted for survival analysis due to their appealing interpretablility and their ability to capture complex relationships. However, most existing methods to produce survival trees rely on heuristic (or greedy) algorithms, which risk producing sub-optimal models. We present a dynamic-programming-with-bounds approach that finds provably-optimal sparse survival tree models, frequently in only a few seconds.

Cite this Paper

BibTeX
@InProceedings{pmlr-v238-zhang24b, title = { Optimal Sparse Survival Trees }, author = {Zhang, Rui and Xin, Rui and Seltzer, Margo and Rudin, Cynthia}, booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics}, pages = {352--360}, year = {2024}, editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}, volume = {238}, series = {Proceedings of Machine Learning Research}, month = {02--04 May}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v238/zhang24b/zhang24b.pdf}, url = {https://proceedings.mlr.press/v238/zhang24b.html}, abstract = { Interpretability is crucial for doctors, hospitals, pharmaceutical companies and biotechnology corporations to analyze and make decisions for high stakes problems that involve human health. Tree-based methods have been widely adopted for survival analysis due to their appealing interpretablility and their ability to capture complex relationships. However, most existing methods to produce survival trees rely on heuristic (or greedy) algorithms, which risk producing sub-optimal models. We present a dynamic-programming-with-bounds approach that finds provably-optimal sparse survival tree models, frequently in only a few seconds. } }
Endnote
%0 Conference Paper %T Optimal Sparse Survival Trees %A Rui Zhang %A Rui Xin %A Margo Seltzer %A Cynthia Rudin %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-zhang24b %I PMLR %P 352--360 %U https://proceedings.mlr.press/v238/zhang24b.html %V 238 %X Interpretability is crucial for doctors, hospitals, pharmaceutical companies and biotechnology corporations to analyze and make decisions for high stakes problems that involve human health. Tree-based methods have been widely adopted for survival analysis due to their appealing interpretablility and their ability to capture complex relationships. However, most existing methods to produce survival trees rely on heuristic (or greedy) algorithms, which risk producing sub-optimal models. We present a dynamic-programming-with-bounds approach that finds provably-optimal sparse survival tree models, frequently in only a few seconds.
APA
Zhang, R., Xin, R., Seltzer, M. & Rudin, C.. (2024). Optimal Sparse Survival Trees . Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:352-360 Available from https://proceedings.mlr.press/v238/zhang24b.html.

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