Collinear datasets augmentation using Procrustes validation sets
This addresses data augmentation for datasets with collinearity, particularly in fields like food science and healthcare, but appears incremental as it builds on existing resampling and modeling techniques.
The paper tackles the problem of augmenting numeric and mixed datasets with collinearity by proposing a method using cross-validation resampling and latent variable modeling, resulting in a reduction of root mean squared error by 1.5 to 3 times for protein prediction in minced meat.
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.