[IPOL discuss] [IPOL announce] new article: Survival Forest for Left-Truncated Right-Censored Data

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2024/466/

Vincent Laurent, and Olivier Vo Van,
Survival Forest for Left-Truncated Right-Censored Data,
Image Processing On Line, 14 (2024), pp. 194–204.
https://doi.org/10.5201/ipol.2024.466

Abstract

The estimation of the lifetime of an industrial equipment or a patient 
is often based on censored data, because the event of interest is 
observed only for a subsample of observations. The use of the Random 
Forest algorithm applied to industrial data is relevant because the 
algorithm presents robust performances in many applications. Coupled 
with survival approaches, it can produce time trajectories for each 
subset of the feature space and thus differentiate observed objects with 
respect to their lifetimes. Our work aims to generalize the existing 
tree-based approach CART applied to left-truncated right-censored data 
to obtain a Random Forest algorithm. We provide a simple API to use such 
algorithm as well as tools to validate a temporal score against censored 
data.




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