Paper Info
Title
Feature Selection for Hybrid Neuro-Logistic Regression Applied to Classification of Remote Sensed Data
Abstract
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.
Year
DOI
Venue
2008
10.1109/HIS.2008.34
HIS
Keywords
Field
DocType
remote sensed data,hybrid neuro-logistic regression applied,lower number,high number,initial covariates,feature selection,accepted method,competitive performance,different method,binary classification,lr method,pu covariates,logistic regression,precision agriculture,classification,data handling,logistics,neural network,evolutionary algorithm,linear model,regression analysis,classification algorithms,feature extraction,neural nets,agriculture,pixel,simulated annealing,wald test,evolutionary algorithms,remote sensing,accuracy
Covariate,Pattern recognition,Feature selection,Binary classification,Linear model,Regression analysis,Wald test,Artificial intelligence,Statistics,Statistical classification,Logistic regression,Mathematics
Conference
Citations 
PageRank 
References 
0
0.34
8
Authors
6
Name
Order
Citations
PageRank
Pedro Antonio Gutiérrez143347.30
Juan Carlos Fernández2729.77
César Hervás318314.38
Francisca López-Granados4423.70
Montserrat Jurado-Expósito5111.64
Jose M. Peña-Barragán6362.89