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Random Forests and Adaptive Nearest Neighbors

Lin, Yi, Jeon, Yongho·2006·Journal of the American Statistical Association

DOI 10.1198/016214505000001230

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Abstract

In this article we study random forests through their connection with a new framework of adaptive nearest-neighbor methods. We introduce a concept of potential nearest neighbors (k-PNNs) and show that random forests can be viewed as adaptively weighted k-PNN methods. Various aspects of random forests can be studied from this perspective. We study the effect of terminal node sizes on the prediction accuracy of random forests. We further show that random forests with adaptive splitting schemes assign weights to k-PNNs in a desirable way: for the estimation at a given target point, these random forests assign voting weights to the k-PNNs of the target point according to the local importance of different input variables. We propose a new simple splitting scheme that achieves desirable adaptivity in a straightforward fashion. This simple scheme can be combined with existing algorithms. The resulting algorithm is computationally faster and gives comparable results. Other possible aspects of random forests, such as using linear combinations in splitting, are also discussed. Simulations and real datasets are used to illustrate the results.

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Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
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Friedman, J. H. (1991) · The Annals of Statistics · doi:10.1214/aos/1176347963 · Cited by 8,108
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Show all 24 references

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Authors: Lin, Y., & Jeon, Y.
Affiliations: University of Wisconsin–Madison (US)
ORCID: Lin, Yi
Venue: Journal of the American Statistical Association
Publisher: Informa UK Limited
Published: 2006-06-01
Volume: 101
Issue: 474
Pages: 578-590
ISSN: 0162-1459 (ISSN-L)
1537-274X
DOI: 10.1198/016214505000001230
OpenAlex: W2047081748
Cited by: 507
References: 24
Citations by year: 2026: 17 · 2025: 31 · 2024: 39 · 2023: 45 · 2022: 35 · 2021: 41 · 2020: 45 · 2019: 49 · 2018: 28 · 2017: 20 · 2016: 24 · 2015: 18 · 2014: 28 · 2013: 14 · 2012: 15
FWCI: 6.29
Type: journal article
Language: en
Open access: closed
Locations: Journal of the American Statistical Association (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords: random forest, voting weights, feature importance, operator splitting, ensemble learning

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Random Forests and Adaptive Nearest Neighbors

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