Parameter Selection Algorithm For Continuous Variables
This work addresses model selection efficiency and stability for regression analysis, but it appears incremental as it builds on existing subset selection methodologies.
The paper tackles the problem of selecting optimal subsets of variables and capturing non-linear relationships in supervised learning by introducing a new algorithm for automatic variable transformation and model selection within the least squares regression framework, resulting in a method that minimizes mean square error and variability while controlling multicollinearity.
In this article, we propose a new algorithm for supervised learning methods, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, an ideal selection method should have the potential of adding a supplementary level of regression analysis that would capture complex relationships in the data via mathematical transformation of the predictors and exploration of synergistic effects of combined variables. The method that we present here has the potential to produce an optimal subset of variables, rendering the overall process of model selection to be more efficient. The core objective of this paper is to introduce a new estimation technique for the classical least square regression framework. This new automatic variable transformation and model selection method could offer an optimal and stable model that minimizes the mean square error and variability, while combining all possible subset selection methodology and including variable transformations and interaction. Moreover, this novel method controls multicollinearity, leading to an optimal set of explanatory variables.