In this paper, we propose a new method for the augmentation of numeric and
mixed datasets. The method generates additional data points by utilizing
cross-validation resampling and latent variable modeling. It is particularly
efficient for datasets with moderate to high degrees of collinearity, as it
directly utilizes this property for generation. The method is simple, fast, and
has very few parameters, which, as shown in the paper, do not require specific
tuning. It has been tested on several real datasets; here, we report detailed
results for two cases, prediction of protein in minced meat based on near
infrared spectra (fully numeric data with high degree of collinearity) and
discrimination of patients referred for coronary angiography (mixed data, with
both numeric and categorical variables, and moderate collinearity). In both
cases, artificial neural networks were employed for developing the regression
and the discrimination models. The results show a clear improvement in the
performance of the models; thus for the prediction of meat protein, fitting the
model to the augmented data resulted in a reduction in the root mean squared
error computed for the independent test set by 1.5 to 3 times.

我们提出了一种新的方法来增加数字和混合数据集，通过利用交叉验证重采样和潜变量建模来生成额外的数据点。该方法对于具有中度到高度共线性的数据集特别有效，因为它直接利用了这个属性进行生成。该方法简单、快速，并且有很少的参数，在文中已经证明无需特定调整。我们对多个真实数据集进行了测试；在这里，我们报告了两种情况的详细结果，即基于近红外光谱的切碎肉中蛋白质的预测（完全数字数据，具有高度共线性）和因冠状动脉造影而被提及的患者的区分（混合数据，包括数字和分类变量，并具有中度共线性）。在这两种情况下，我们使用人工神经网络来开发回归和区分模型。结果显示模型性能有明显提高；因此，在预测肉类蛋白质时，拟合增强数据的模型可以将独立测试集的均方根误差减少 1.5 至 3 倍。