k-Nearest Neighbour Classifiers - A Tutorial

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 runtime performance is not such a problem these days with the computational power that is available. This article 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 article is the second edition of a paper previously published as a technical report [16]. Sections on similarity measures for time-series, retrieval speedup, and intrinsic dimensionality have been added. An Appendix is included, providing access to Python code for the key methods.

Automated summary

The article provides an overview of Nearest Neighbour classification techniques, focusing on similarity assessment mechanisms, computational issues in identifying nearest neighbours, and methods for reducing the dimension of the data. New sections on similarity measures for time-series, retrieval speedup, and intrinsic dimensionality have been added, along with an Appendix containing Python code for key methods.