Medoid splits for efficient random forests in metric spaces
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Medoid splits for efficient random forests in metric spaces. / Bulté, Matthieu; Sørensen, Helle.
I: Computational Statistics and Data Analysis, Bind 198, 107995, 2024.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Medoid splits for efficient random forests in metric spaces
AU - Bulté, Matthieu
AU - Sørensen, Helle
N1 - Publisher Copyright: © 2024 The Authors
PY - 2024
Y1 - 2024
N2 - An adaptation of the random forest algorithm for Fréchet regression is revisited, addressing the challenge of regression with random objects in metric spaces. To overcome the limitations of previous approaches, a new splitting rule is introduced, substituting the computationally expensive Fréchet means with a medoid-based approach. The asymptotic equivalence of this method to Fréchet mean-based procedures is demonstrated, along with the consistency of the associated regression estimator. This approach provides a sound theoretical framework and a more efficient computational solution to Fréchet regression, broadening its application to non-standard data types and complex use cases.
AB - An adaptation of the random forest algorithm for Fréchet regression is revisited, addressing the challenge of regression with random objects in metric spaces. To overcome the limitations of previous approaches, a new splitting rule is introduced, substituting the computationally expensive Fréchet means with a medoid-based approach. The asymptotic equivalence of this method to Fréchet mean-based procedures is demonstrated, along with the consistency of the associated regression estimator. This approach provides a sound theoretical framework and a more efficient computational solution to Fréchet regression, broadening its application to non-standard data types and complex use cases.
KW - Least squares regression
KW - Medoid
KW - Metric spaces
KW - Random forest
KW - Random objects
U2 - 10.1016/j.csda.2024.107995
DO - 10.1016/j.csda.2024.107995
M3 - Journal article
AN - SCOPUS:85196724062
VL - 198
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
SN - 0167-9473
M1 - 107995
ER -
ID: 396942515