Optimal Sparse Survival Trees.
Publication
, Conference
Zhang, R; Xin, R; Seltzer, M; Rudin, C
Published in: Proceedings of machine learning research
May 2024
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.
Duke Scholars
Published In
Proceedings of machine learning research
EISSN
2640-3498
ISSN
2640-3498
Publication Date
May 2024
Volume
238
Start / End Page
352 / 360
Citation
APA
Chicago
ICMJE
MLA
NLM
Zhang, R., Xin, R., Seltzer, M., & Rudin, C. (2024). Optimal Sparse Survival Trees. In Proceedings of machine learning research (Vol. 238, pp. 352–360).
Zhang, Rui, Rui Xin, Margo Seltzer, and Cynthia Rudin. “Optimal Sparse Survival Trees.” In Proceedings of Machine Learning Research, 238:352–60, 2024.
Zhang R, Xin R, Seltzer M, Rudin C. Optimal Sparse Survival Trees. In: Proceedings of machine learning research. 2024. p. 352–60.
Zhang, Rui, et al. “Optimal Sparse Survival Trees.” Proceedings of Machine Learning Research, vol. 238, 2024, pp. 352–60.
Zhang R, Xin R, Seltzer M, Rudin C. Optimal Sparse Survival Trees. Proceedings of machine learning research. 2024. p. 352–360.
Published In
Proceedings of machine learning research
EISSN
2640-3498
ISSN
2640-3498
Publication Date
May 2024
Volume
238
Start / End Page
352 / 360