An Inverse-Based Algorithm for Solving Multicollinearity Problem

The rapid evolution of information technology is significantly fueling the growing demand for machine learning methodologies, and regression analysis is emerging as a particularly crucial component in this landscape. Considering the unprecedented growth in the volume of available data, it has become imperative to identify and select the most relevant features to develop effective models. A challenge that can adversely affect model quality is multicollinearity, which is characterized by a high degree of linear dependence among features. Despite the wealth of research in this area, effectively addressing these challenges often proves to be a time-consuming and inefficient endeavor. This paper introduces a straightforward algorithm designed to address the issue of multicollinearity, thereby optimizing data processing efficiency while preserving model quality. To achieve this objective, we propose a method that involves reformulating the existing model into a single-point inverse problem, coupled with a modification of the algorithm to enhance the effectiveness of the solution. The experimental results, which were derived from an analysis of both synthetic and real-world data, demonstrate the effectiveness of the proposed algorithm.