Author ORCID Identifier

https://orcid.org/ 0000-0002-2062-7439

Document Type

Article

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Computer Sciences

Abstract

Perhaps the most straightforward classifier in the arsenal or Machine Learning techniques is the Nearest Neighbour Classifier – classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance because issues of poor run-time performance is not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours and mechanisms for reducing the dimension of the data.
This paper is the second edition of a paper previously published as a technical report . Sections on similarity measures for time-series, retrieval speed-up and intrinsic dimensionality have been added. An Appendix is included providing access to Python code for the key methods.

DOI

https://doi.org/10.1145/3459665

Funder

SFI

Download
Contact the Author


Plum Print visual indicator of research metrics
PlumX Metrics
  • Citations
    • Citation Indexes: 550
    • Policy Citations: 1
  • Usage
    • Downloads: 741
    • Abstract Views: 38
  • Captures
    • Readers: 1036
see details

Share

COinS