Machine Learning Training Optimization using the Barycentric Correction
  Procedure
Sofia Ramos-Pulido Neil Hernandez-Gress Hector G. Ceballos-Cancino
Machine learning (ML) algorithms are predictively competitive algorithms with
many human-impact applications. However, the issue of long execution time
remains unsolved in the literature for high-dimensional spaces. This study
proposes combining ML algorithms with an efficient methodology known as the
barycentric correction procedure (BCP) to address this issue. This study uses
synthetic data and an educational dataset from a private university to show the
benefits of the proposed method. It was found that this combination provides
significant benefits related to time in synthetic and real data without losing
accuracy when the number of instances and dimensions increases. Additionally,
for high-dimensional spaces, it was proved that BCP and linear support vector
classification (LinearSVC), after an estimated feature map for the gaussian
radial basis function (RBF) kernel, were unfeasible in terms of computational
time and accuracy.