A Correlation-Based Sorting Algorithm of Inductive Modeling using Argument Rating

In this paper, a sparse regression algorithm is proposed that corresponds to a linear model with the smallest possible number of regressors without loss of quality of the model based on correlation analysis and using an external criterion for choosing an optimal model based on the division of the input data sample. That is why the proposed method relates to the class of GMDH algorithms. The main stages of the algorithm work are described. The core feature of the algorithm is the use of frequency analysis of regressor ratings. The results of testing its effectiveness on test experiments are shown in comparison with the known algorithm LASSO.