NDLI: Feature Selection for Hybrid Neuro-Logistic Regression Applied to Classification of Remote Sensed Data

Content Provider	IEEE Xplore Digital Library
Author	Gutierrez, P.A. Fernandez, J.C. Hervas, C. Lopez-Granados, F. Jurado-Exposito, M. Pena-Barragan, J.M.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Numerical Anal., Univ. of Cordoba, Cordoba (Gutierrez, P.A.; Fernandez, J.C.; Hervas, C.)
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.
Starting Page	625
Ending Page	630
File Size	230108
Page Count	6
File Format	PDF
ISBN	9780769533261
DOI	10.1109/HIS.2008.34
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-10
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	multi-spectral imagery Machine learning algorithms product unit neural networks evolutionary algorithms remote sensing Classification algorithms classification Remote sensing precision agriculture Accuracy Agriculture logistic regression Pixel Logistics
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in