Machine Learning Training Optimization using the Barycentric Correction Procedure

arXiv:2403.00542v1 Announce Type: new
Abstract: 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 …

abstract algorithms applications arxiv cs.lg human impact issue literature machine machine learning methodology ml algorithms optimization spaces study training type unsolved