k-Nearest Neighbour Classifiers - A Tutorial

🤖 Plain-English Summary

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. Sections on similarity measures for time-series, retrieval speedup, and intrinsic dimensionality have been added.

🔑 Key Findings

• 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 .

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