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Journal of Classification
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30-03-2019

On Cesáro Averages for Weighted Trees in the Random Forest

Authors: Hieu Pham, Sigurður Olafsson

Published in: Journal of Classification | Issue 1/2020

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Abstract

The random forest is a popular and effective classification method. It uses a combination of bootstrap resampling and subspace sampling to construct an ensemble of decision trees that are then averaged for a final prediction. In this paper, we propose a potential improvement on the random forest that can be thought of as applying a weight to each tree before averaging. The new method is motivated by the potential instability of averaging predictions of trees that may be of highly variable quality, and because of this, we replace the regular average with a Cesáro average. We provide both a theoretical analysis that gives exact conditions under which the new approach outperforms the traditional random forest, and numerical analysis that shows the new approach is competitive when training a classification model on numerous realistic data sets.
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Literature
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Metadata
Title
On Cesáro Averages for Weighted Trees in the Random Forest
Authors
Hieu Pham
Sigurður Olafsson
Publication date
30-03-2019
Publisher
Springer US
Published in
Journal of Classification / Issue 1/2020
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-019-09322-8

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