# A Random Projection k Nearest Neighbours Ensemble for Classification via Extended Neighbourhood Rule

arXiv: https://arxiv.org/abs/2303.12210 · HTML: https://mlsanity.com/paper/2303.12210/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 73 references that are in the map.
- First posted on arXiv 21 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)](https://mlsanity.com/paper/2004.04523/) (2020)
- [A k nearest neighbours classifiers ensemble based on extended neighbourhood rule and features subsets](https://mlsanity.com/paper/2205.15111/) (2022)

## Abstract (arXiv)

Ensembles based on k nearest neighbours (kNN) combine a large number of base learners, each constructed on a sample taken from a given training data. Typical kNN based ensembles determine the k closest observations in the training data bounded to a test sample point by a spherical region to predict its class. In this paper, a novel random projection extended neighbourhood rule (RPExNRule) ensemble is proposed where bootstrap samples from the given training data are randomly projected into lower dimensions for additional randomness in the base models and to preserve features information. It uses the extended neighbourhood rule (ExNRule) to fit kNN as base learners on randomly projected bootstrap samples.