mlsanityData to 14 Oct 2026

k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)

arXiv 2004.04523 ↗First posted 9 Apr 2020cs.LG stat.MLcited by 56

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Abstract (arXiv)

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.

Key facts. Cited by 56 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Apr 2020.

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Citing papers per year: 2021: 3; 2022: 8; 2023: 12; 2024: 13; 2025: 15; 2026: 5.

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