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Previous issue date: 2013-05-03 / This work proposes the use of multi-objective genetics algorithms NSGA-II and SPEA-II
on the variable selection in multivariate calibration problems. These algorithms are used
for selecting variables for a Multiple Linear Regression (MLR) by two conflicting objectives:
the prediction error and the used variables number in MLR. For the case study
are used wheat data obtained by NIR spectrometry with the objective for determining a
variable subgroup with information about protein concentration. The results of traditional
techniques of multivariate calibration as the Partial Least Square (PLS) and Successive
Projection Algorithm (SPA) for MLR are presents for comparisons. The obtained
results showed that the proposed approach obtained better results when compared with
a monoobjective evolutionary algorithm and with traditional techniques of multivariate
calibration. / Este trabalho propõe a utilização dos algoritmos genéticos multiobjetivo NSGA-II e
SPEA-II na seleção de variáveis em problemas de calibração multivariada. Esses algoritmos
são utilizados para selecionar variáveis para Regressão Linear Múltipla (MLR)
com dois objetivos conflitantes: o erro de predição e do número de variáveis utilizadas na
MLR. Para o estudo de caso são usado dados de trigo obtidos por espectrometria NIR com
o objetivo de determinar um subgrupo de variáveis com informações sobre a concentração
de proteína. Os resultados das técnicas tradicionais de calibração multivariada como dos
Mínimos Quadrados Parciais (PLS) e Algoritmo de Projeções Sucessivas (APS) para a
MLR estão presentes para comparações. Os resultados obtidos mostraram que a abordagem
proposta obteve melhores resultados quando comparado com um algoritmo evolutivo
monoobjetivo e com as técnicas tradicionais de calibração multivariada.
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.bc.ufg.br:tede/3096 |
| Date | 03 May 2013 |
| Creators | Lucena, Daniel Vitor de |
| Contributors | Soares, Telma Woerle de Lima |
| Publisher | Universidade Federal de Goiás, Programa de Pós-graduação em Ciência da Computação (INF), UFG, Brasil, Instituto de Informática - INF (RG) |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Source | reponame:Biblioteca Digital de Teses e Dissertações da UFG, instname:Universidade Federal de Goiás, instacron:UFG |
| Rights | http://creativecommons.org/licenses/by-nc-nd/4.0/, info:eu-repo/semantics/openAccess |
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