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