AISTATS 2024poster10 citations
Optimal Sparse Survival Trees
Rui Zhang, Rui Xin, Margo Seltzer, Cynthia Rudin
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.
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.}
}