Feature Selection for Hybrid Neuro-Logistic Regression Applied to Classification of Remote Sensed Data
Pedro Antonio Gutiérrez, Juan Carlos Fernández Fernández, César Hervás‐Martínez, Francisca López Granados, Montserrat Jurado-Expósito, José Manuel Peña · 2008
Logistic Regression (LR) has become a widely used and accepted method to analyse binary or multiclass outcome variables, since it is a flexible tool that can predict the probability for the state of a dichotomous variable. A recently proposed LR method is based on the hybridisation of a linear model and Evolutionary Product-Unit Neural Network (EPUNN) models for binary classification. This produces a high number of coefficients, so two different methods for reducing the number of initial or PU covariates are proposed in this paper, both based on the Wald test. The first method is a two-step Backward Search (BS) method and the second is based on the standard Simulated Annealing (SA) heuristic. In this study, we used aerial imagery taken in mid-May to evaluate the potential of two different combinations of LR and EPUNN (LR using PUs (LRPU), as well as LR using Initial covariates and PUs (LRIPU)) and the two proposed methods for selecting variables in the final models (BS and SA) for discriminating Ridolfia segetum patches (one of the most dominant, competitive and persistent weed in sunflower crops) in one naturally infested field of southern Spain. Then, we compared the performance of these methods to six recent classification models, our proposals obtaining a competitive performance and a lower number of coefficients.