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Improved nearest neighbor classifiers by weighting and selection of predictors

机译：通过对预测变量进行加权和选择来改进最近邻分类器

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摘要

Nearest neighborhood classification is a flexible classification method that works under weak assumptions. The basic concept is to use the weighted or un-weighted sums over class indicators of observations in the neighborhood of the target value. Two modifications that improve the performance are considered here. Firstly, instead of using weights that are solely determined by the distances we estimate the weights by use of a logit model. By using a selection procedure like lasso or boosting the relevant nearest neighbors are automatically selected. Based on the concept of estimation and selection, in the second step, we extend the predictor space. We include nearest neighborhood counts, but also the original predictors themselves and nearest neighborhood counts that use distances in sub dimensions of the predictor space. The resulting classifiers combine the strength of nearest neighbor methods with parametric approaches and by use of sub dimensions are able to select the relevant features. Simulations and real data sets demonstrate that the method yields better misclassification rates than currently available nearest neighborhood methods and is a strong and flexible competitor in classification problems.
机译：最近邻域分类是一种灵活的分类方法，可在较弱的假设下使用。基本概念是对目标值附近的观测类别指标使用加权或未加权总和。这里考虑改善性能的两个修改。首先，不是使用仅由距离确定的权重，而是通过使用logit模型来估计权重。通过使用套索或增强等选择过程，可以自动选择相关的最近邻居。基于估计和选择的概念，在第二步中，我们扩展了预测变量空间。我们不仅包括最近的邻域计数，还包括原始预测变量本身以及在预测变量空间的子维中使用距离的最近邻域计数。生成的分类器将最近邻方法的优势与参数方法结合在一起，并且通过使用子维度可以选择相关特征。仿真和真实数据集表明，该方法比当前可用的最近邻域方法产生更好的误分类率，并且在分类问题上是强大而灵活的竞争者。

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