Random Forests for Regression as a Weighted Sum of <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-Potential Nearest Neighbors

Random Forests for Regression as a Weighted Sum of <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-Potential Nearest Neighbors

In this paper, we tackle the problem of random forests for regression expressed as weighted sums of datapoints. We study the theoretical behavior of k-potential nearest neighbors (k-PNNs) under bagging and obtain an upper bound on the weights of a datapoint for random forests with any type of splitt...

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Bibliographic Details
Main Authors: Pablo Fernandez-Gonzalez, Concha Bielza, Pedro Larranaga
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Random forests
regression
bagging
bootstrap
nearest neighbors
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-potential nearest neighbors
Online Access:https://ieeexplore.ieee.org/document/8648334/

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https://ieeexplore.ieee.org/document/8648334/

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