Multinomial logistic regression and product unit neural network models: Application of a new hybrid methodology for solving a classification problem in the livestock sector

Abstract

This work presents a new approach for multi-class pattern recognition based on the hybridization of a linear and nonlinear model. We propose multinomial logistic regression where some new covariates are defined by a product unit neural network, where in turn, the nonlinear basis functions are constructed with the product of the inputs raised to arbitrary powers. The application of this methodology involves, first of all, training the coefficients and the basis structure of product unit models using techniques based on artificial neural networks and evolutionary algorithms, followed by the application of multinomial logistic regression to both the new derived features and the original ones.