Instance selection aims to search for the best patterns in the training set and main instance selection methods include condensation methods, edition methods and hybrid methods. Hybrid methods combine advantages of both edition methods and condensation methods. Nevertheless, most of existing hybrid approaches heavily rely on parameters and are relatively time-consuming, resulting in the performance instability and application difficulty. Though several relatively fast and (or) parameter-free hybrid methods are proposed, they still have the difficulty in achieving both high accuracy and high reduction. In order to solve these problems, we present a new parameter-free hybrid instance selection algorithm based on local sets with natural neighbors (LSNaNIS). A new parameter-free definition for the local set is first proposed based on the fast search for natural neighbors. The new local set can fast and reasonably describe local characteristics of data. In LSNaNIS, we use the new local set to design an edition method (LSEdit) to remove harmful samples, a border method (LSBorder) to retain representative border samples and a core method (LSCore) to condense internal samples. Comparison experiments show that LSNaNIS is relatively fast and outperforms existing hybrid methods in improving the k-nearest neighbor in terms of both accuracy and reduction.

实例选择旨在搜索训练集中的最佳模式，而主要实例选择方法包括压缩方法，编辑方法和混合方法。混合方法结合了编辑方法和缩合方法的优点。然而，大多数现有的混合方法都严重依赖参数并且相对耗时，从而导致性能不稳定和应用困难。尽管提出了几种相对较快和（或）无参数的混合方法，但它们仍然难以同时实现高精度和高简化率。为了解决这些问题，我们提出了一种新的基于自然邻居的本地集的无参数混合实例选择算法（LSNaNIS）。首先基于对自然邻居的快速搜索，提出了一种针对本地集的新的无参数定义。新的本地集可以快速合理地描述数据的本地特征。在LSNaNIS中，我们使用新的本地集来设计编辑方法（LSEdit）来删除有害样本，使用边界方法（LSBorder）保留代表性的边界样本，并使用核心方法（LSCore）来浓缩内部样本。对比实验表明，LSNaNIS相对较快，并且在改善分辨率方面优于现有的混合方法。边界方法（LSBorder）保留代表性的边界样本，核心方法（LSCore）浓缩内部样本。对比实验表明，LSNaNIS相对较快，并且在改善分辨率方面优于现有的混合方法。边界方法（LSBorder）保留代表性的边界样本，核心方法（LSCore）浓缩内部样本。对比实验表明，LSNaNIS相对较快，并且在改善分辨率方面优于现有的混合方法。就准确性和减少而言，k-最近邻。