In this paper we introduce a simple and intuitive adaptive k nearest neighbours classifier, and explore its utility within the context of bootstrap aggregating ("bagging"). The approach is based on finding discriminant subspaces which are computationally efficient to compute, and are motivated by enhancing the discrimination of classes through nearest neighbour classifiers. This adaptiveness promotes diversity of the individual classifiers fit across different bootstrap samples, and so further leverages the variance reducing effect of bagging. Extensive experimental results are presented documenting the strong performance of the proposed approach in comparison with Random Forest classifiers, as well as other nearest neighbours based ensembles from the literature, plus other relevant benchmarks. Code to implement the proposed approach is available in the form of an R package from https://github.com/DavidHofmeyr/BOPNN.

翻译：本文提出了一种简单直观的自适应k最近邻分类器，并探讨了其在自助聚合（"装袋法"）框架下的应用价值。该方法通过寻找计算高效的判别子空间来实现，其动机在于通过最近邻分类器增强类别区分能力。这种自适应性促进了不同自助样本上训练的基分类器的多样性，从而进一步放大了装袋法降低方差的效果。大量实验结果表明，与随机森林分类器、文献中其他基于最近邻的集成方法以及其他相关基准模型相比，所提方法均表现出优越性能。该方法的实现代码已封装为R软件包，可通过https://github.com/DavidHofmeyr/BOPNN获取。