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Properties of bagged nearest neighbour classifiers

Date

Authors

Hall, Peter

Samworth, Richard

Publisher

Aiden Press

Abstract

It is shown that bagging, a computationally intensive method, asymptotically improves the performance of nearest neighbour classifiers provided that the resample size is less than 69% of the actual sample size, in the case of with-replacement bagging, or

Keywords

Keywords: Bayes risk; Bootstrap; Classification error; Cross-validation; Density; Discrimination; Error rate; Marked point process; Poisson process; Prediction; Regret; Statistical learning; With-replacement sampling; Without-replacement sampling

URI

http://hdl.handle.net/1885/82020

Collections

ANU Research Publications

Source

Journal of the Royal Statistical Society Series B

Type

Journal article

DOI

10.1111/j.1467-9868.2005.00506.x

Restricted until

2037-12-31

Downloads

File

Description

01_Hall_Properties_of_bagged_nearest_2005.pdf (290.44 KB)

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