Optimal and rational choice

An algorithm for selecting linear regression features to solve the multicollinearity problem

E. B. Gribanova

Tomsk State University of Control Systems and Radioelectronics, Tomsk, Russia

Abstract: The paper considers the problem of selecting linear regression factors using an optimization model that includes characteristics of the relationship of features, as well as the dependence of the feature and the effective indicator. To solve it, it is proposed to reformulate the original problem in the form of an inverse while minimizing the sum of the absolute values of the arguments. The results of computational experiments, including comparison with nonlinear programming methods implemented in mathematical packages and the Python library, demonstrated the high efficiency of the proposed algorithm for solving the modified problem.

Keywords: feature selection, inverse problem, linear regression, nonlinear programming, optimization algorithm, multicollinearity.

DOI: 10.14357/20718594250104

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