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.

翻译：基于k最近邻（kNN）的集成方法融合了大量基学习器，每个基学习器均在给定训练数据的采样样本上构建而成。典型的kNN集成方法通过球形区域界定训练数据中与测试样本点最近的k个观测值来预测其类别。本文提出一种新颖的随机投影扩展邻域规则（RPExNRule）集成方法，该方法对给定训练数据的自助采样样本进行随机投影至低维空间，以增强基模型的随机性并保留特征信息。该方法采用扩展邻域规则（ExNRule）将kNN拟合为基学习器，并在随机投影后的自助采样样本上进行训练。