Modernizing k‐nearest neighbors

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 09 Jun 2021 English Publisher:WileyJournal:Stat, volume 10 (issn: 2049-1573, eissn: 2049-1573,

Copyright policy )

Authors: Robin Elizabeth Yancey; Bochao Xin; Norm Matloff;

doi: 10.1002/sta4.335

Modernizing k‐nearest neighbors

- Summary
- Subjects
- Metrics

Abstract

The k‐nearest neighbors (k‐NN) method is one of the oldest statistical/machine learning techniques. It is included in virtually every major package, such as caret, parsnip, mlr3 and scikit‐learn. Yet those packages do not go beyond the basics. With today's high‐speed computation capability, k‐NN can be made much more powerful. Here, we present directions in which that can be done.

Related Organizations

University of California, Davis
United States

Keywords

Statistics, k-nearest neighbor, local bandwidth, Mahalanobis distance, local linear

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	3
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

Found an issue? Give us feedback

3

Top 10%

Average

gold

Fields of Science (4) View all

Fields of Science