Instance selection and construction methods were originally designed to improve the performance of the k-nearest neighbors classifier by increasing its speed and improving the classification accuracy. These goals were achieved by eliminating redundant and noisy samples, thus reducing the size of the training set. In this paper, the performance of instance selection methods is investigated in terms of classification accuracy and reduction of training set size. The classification accuracy of the following classifiers is evaluated: decision trees, random forest, Naive Bayes, linear model, support vector machine and k-nearest neighbors. The obtained results indicate that for the most of the classifiers compressing the training set affects prediction performance and only a small group of instance selection methods can be recommended as a general purpose preprocessing step. These are learning vector quantization based algorithms, along with the Drop2 and Drop3 . Other methods are less efficient or provide low compression ratio.

中文翻译：

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各种分类器的实例选择和构造方法比较

实例选择和构造方法最初旨在通过提高k最近邻分类器的速度和提高分类精度来提高其性能。这些目标是通过消除多余和嘈杂的样本来实现的，从而减小了训练集的大小。本文从分类的准确性和训练集规模的减少方面研究了实例选择方法的性能。评估以下分类器的分类准确性：决策树，随机森林，朴素贝叶斯，线性模型，支持向量机和k最近邻。获得的结果表明，对于大多数分类器而言，训练集的压缩会影响预测性能，因此只能推荐一小部分实例选择方法作为通用预处理步骤。这些是基于学习矢量量化的算法，以及Drop2和Drop3。其他方法效率较低或压缩率较低。