Algoritmos evolutivo multiobjetivo para seleção de variáveis em problemas de calibração multivariada
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Bibliographic Details
| Main Author: |
Lucena, Daniel Vitor de |
| Publication Date: |
2013 |
| Format: |
Master thesis
|
| Language: |
por |
| Source: |
Repositório Institucional da UFG |
| Download full: |
http://repositorio.bc.ufg.br/tede/handle/tede/3096
|
Summary: |
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. |
