Algoritmos evolutivo multiobjetivo para seleção de variáveis em problemas de calibração multivariada
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Data
2013-05-03
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Universidade Federal de Goiás
Resumo
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.
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Citação
LUCENA, Daniel Vitor de. Algoritmos evolutivo multiobjetivo para seleção de variáveis em problemas de calibração multivariada. 2013. 55 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2013.