Machine Learning Training Optimization using the Barycentric Correction Procedure
This addresses the issue of slow training times in machine learning for researchers and practitioners, but it appears incremental as it combines existing methods without a major breakthrough.
The study tackled the problem of long execution times in machine learning for high-dimensional spaces by combining ML algorithms with the barycentric correction procedure (BCP), resulting in significant time benefits without accuracy loss as instances and dimensions increased, though BCP with LinearSVC and an estimated RBF kernel was found unfeasible in terms of computational time and accuracy for high-dimensional spaces.
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.