Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier -- A Review

TL;DR

This review evaluates how different distance and similarity measures affect KNN classifier performance across various datasets and noise levels, highlighting the superior robustness of a recent non-convex distance.

Contribution

It systematically compares numerous distance measures for KNN, identifying the most effective and noise-tolerant distances through extensive experiments.

Findings

Non-convex distance performed best across datasets.

KNN performance varies significantly with distance measure.

Top distances tolerate up to 90% noise with only 20% performance degradation.

Abstract

The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested examples and the training examples. This raises a major question about which distance measures to be used for the KNN classifier among a large number of distance and similarity measures available? This review attempts to answer this question through evaluating the performance (measured by accuracy, precision and recall) of the KNN using a large number of distance measures, tested on a number of real-world datasets, with and without adding different levels of noise. The experimental results show that the performance of KNN classifier depends significantly on the distance used, and the results showed large gaps between the performances of different distances. We found that a recently proposed non-convex distance performed the best when applied on most datasets comparing to the other tested distances. In addition, the performance of the KNN with this top performing distance degraded only about $20\%$ while the noise level reaches $90\%$, this is true for most of the distances used as well. This means that the KNN classifier using any of the top $10$ distances tolerate noise to a certain degree. Moreover, the results show that some distances are less affected by the added noise comparing to other distances.