PSR: PSO-based signomial regression model

Abstract

Regression analysis can be used for predictive and descriptive purposes in a variety of business applications. However, the successive existing regression methods such as support vector regression (SVR) have the drawback that it is not easy to derive an explicit function description that expresses the nonlinear relationship between an output variable and input variables. To resolve this issue, developed in this article is a nonlinear regression algorithm using particle swarm optimization (PSO) which is PSR. The output variables of PSR allow to obtain the explicit function description of the output variable using input variables. Three PSRs are proposed based on infeasible-particle update rules. Their experimental results show that the proposed approach performs similarly to and slightly better than the existing methods regardless of the data sets, implying that it can be utilized as a useful alternative when obtaining the explicit function description of the output variable using input variables and interpreting which of the original input variables are more important than others in the obtained regression model.