Variable Selection through Genetic Algorithms for Classification Purposes

2010 ◽  
Author(s):  
S. Cateni ◽  
V. Colla ◽  
M. Vannucci
Keyword(s):  
Genetic Algorithms ◽  
Variable Selection

Variable selection in STAR models with neighbourhood effects using genetic algorithms

2010 ◽  
Vol 29 (8) ◽  
pp. 728-750 ◽  
Author(s):  
Isolina Alberto ◽  
Asunción Beamonte ◽  
Pilar Gargallo ◽  
Pedro M. Mateo ◽  
Manuel Salvador
Keyword(s):  
Genetic Algorithms ◽  
Variable Selection ◽  
Neighbourhood Effects ◽  
Star Models

Several Approaches to Variable Selection by Means of Genetic Algorithms

2011 ◽  
pp. 274-292
Author(s):  
Marcos Gestal Pose ◽  
Alberto Cancela Carollo ◽  
José Manuel Andrade Garda ◽  
Mari Paz Gomez-Carracedo
Keyword(s):  
Analytical Chemistry ◽  
Genetic Algorithms ◽  
Variable Selection ◽  
Evolutionary Computation ◽  
Fitness Function ◽  
Classification Model ◽  
Classification Task ◽  
Practical Case ◽  
General Method

This chapter shows several approaches to determine how the most relevant subset of variables can perform a classification task. It will permit the improvement and efficiency of the classification model. A particular technique of evolutionary computation, the genetic algorithms, is applied which aim to obtain a general method of variable selection where only the fitness function will be dependent on the particular problem. The solution proposed is applied and tested on a practical case in the field of analytical chemistry to classify apple beverages.

Chemically driven variable selection by focused multimodal genetic algorithms in mid-IR spectra

2007 ◽  
Vol 389 (7-8) ◽  
pp. 2331-2342 ◽  
Author(s):  
M. P. Gómez-Carracedo ◽  
M. Gestal ◽  
J. Dorado ◽  
J. M. Andrade
Keyword(s):  
Genetic Algorithms ◽  
Variable Selection ◽  
Ir Spectra

Genetic Algorithms for Variable Selection and Pattern Recognition

2020 ◽  
pp. 673-700
Author(s):  
B.K. Lavine ◽  
Collin G. White ◽  
Charles E. Davidson
Keyword(s):  
Pattern Recognition ◽  
Genetic Algorithms ◽  
Variable Selection

Fuzzy lagged variable selection in fuzzy time series with genetic algorithms

2014 ◽  
Vol 22 ◽  
pp. 465-473 ◽  
Author(s):  
Cagdas Hakan Aladag ◽  
Ufuk Yolcu ◽  
Erol Egrioglu ◽  
Eren Bas
Keyword(s):  
Time Series ◽  
Genetic Algorithms ◽  
Variable Selection ◽  
Fuzzy Time Series

On the use of niching genetic algorithms for variable selection in solar radiation estimation

2013 ◽  
Vol 50 ◽  
pp. 168-176 ◽  
Author(s):  
A. Will ◽  
J. Bustos ◽  
M. Bocco ◽  
J. Gotay ◽  
C. Lamelas
Keyword(s):  
Genetic Algorithms ◽  
Solar Radiation ◽  
Variable Selection

scholarly journals GALGO: an R package for multivariate variable selection using genetic algorithms

2006 ◽  
Vol 22 (9) ◽  
pp. 1154-1156 ◽  
Author(s):  
Victor Trevino ◽  
Francesco Falciani
Keyword(s):  
Genetic Algorithms ◽  
Variable Selection ◽  
R Package

Several Approaches to Variable Selection by Means of Genetic Algorithms

2006 ◽  
pp. 141-165
Author(s):  
Marcos G. Pose ◽  
Alberto C. Carollo ◽  
José M.A. Garda ◽  
Mari P. Gomez-Carracedo
Keyword(s):  
Analytical Chemistry ◽  
Genetic Algorithms ◽  
Variable Selection ◽  
Evolutionary Computation ◽  
Fitness Function ◽  
Classification Model ◽  
Classification Task ◽  
Practical Case ◽  
General Method

This chapter shows several approaches to determine how the most relevant subset of variables can perform a classification task. It will permit the improvement and efficiency of the classification model. A particular technique of evolutionary computation, the genetic algorithms, is applied which aim to obtain a general method of variable selection where only the fitness function will be dependent on the particular problem. The solution proposed is applied and tested on a practical case in the field of analytical chemistry to classify apple beverages.

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