A Random Projection k Nearest Neighbours Ensemble for Classification via Extended Neighbourhood Rule
Machine Learning
2023-03-23 v1 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2303.12210,
title = {A Random Projection k Nearest Neighbours Ensemble for Classification via Extended Neighbourhood Rule},
author = {Amjad Ali and Muhammad Hamraz and Dost Muhammad Khan and Wajdan Deebani and Zardad Khan},
journal= {arXiv preprint arXiv:2303.12210},
year = {2023}
}
Comments
23 pages, 8 diagrams, 69 references