k-Nearest Neighbour Classifiers - A Tutorial

• Published in: ACM computing surveys Vol. 54; no. 6; pp. 1 - 25
• Main Authors: Cunningham, Pádraig, Delany, Sarah Jane
• Format: Journal Article
• Language: English
• Published: Baltimore Association for Computing Machinery 31.07.2022
• Subjects: Classifiers; Computer science; K-nearest neighbors algorithm; Machine learning; Similarity
• ISSN: 0360-0300; 1557-7341
• DOI: 10.1145/3459665

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.