投影最近邻袋：随机森林的竞争者吗？

本文引入了一种简单直观的自适应 k 最近邻分类器，并探讨了其在 bootstrap 聚合（"包袋"）上下文中的实用性。该方法基于寻找判别子空间，这些子空间计算上相对高效，旨在通过最近邻分类器增强类别的判别能力。这种自适应性促进了在不同 bootstrap 样本上所拟合的单个分类器之间的多样性，从而进一步利用包袋的方差降低效应。 presented extensive experimental results documenting the strong performance of the proposed approach compared 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。