Data reduction processes are designed not only to reduce the amount of data, but also to reduce noise interference. In this study, we focus on researching sample reduction algorithms for the classification and regression data. A sample quality evaluation measure denoted by NN-kNN, which is inspired by human social behavior, is proposed. This measure is a local evaluation method that can accurately evaluate the quality of samples under uneven and irregular data distribution. Additionally, the measure is easy to understand and applies to both supervised and unsupervised data. Consequently, it respectively studies the sample reduction algorithms based on the NN-kNN measure for classification and regression data. Experiments are carried out to verify the proposed quality evaluation measure and data reduction algorithms. Experimental results show that NN-kNN can evaluate data quality effectively. High quality samples selected by the reduction algorithms can generate high classification and prediction performance. Furthermore, the robustness of the sample reduction algorithms is also validated.

数据缩减过程的设计不仅可以减少数据量，还可以减少噪声干扰。在这项研究中，我们专注于研究用于分类和回归数据的样本约简算法。提出了一种以人类社会行为为灵感的，以NN- k NN表示的样本质量评估方法。该措施是一种本地评估方法，可以在不均匀和不规则的数据分布下准确评估样品的质量。此外，该度量易于理解，并且适用于受监管的数据和不受监管的数据。因此，分别研究了基于NN- k的样本约简算法。用于分类和回归数据的NN度量。进行实验以验证所提出的质量评估措施和数据缩减算法。实验结果表明，NN- k NN可以有效地评估数据质量。通过归约算法选择的高质量样本可以生成高分类和预测性能。此外，样本缩减算法的鲁棒性也得到了验证。