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

• Published in: arXiv.org
• Main Authors: Cunningham, Padraig, Delany, Sarah Jane
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 29.04.2020
• Subjects: Classifiers; Computer Science - Learning; K-nearest neighbors algorithm; Machine learning; Python; Similarity; Statistics - Machine Learning
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2004.04523

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.