Optimal Sparse Survival Trees
Machine Learning
2024-05-24 v3
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
@article{arxiv.2401.15330,
title = {Optimal Sparse Survival Trees},
author = {Rui Zhang and Rui Xin and Margo Seltzer and Cynthia Rudin},
journal= {arXiv preprint arXiv:2401.15330},
year = {2024}
}
Comments
AISTATS2024 camera ready version. arXiv admin note: text overlap with arXiv:2211.14980