Algoritmo evolutivo de cromossomo duplo para calibração multivariada
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Data
2013-03-05
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Universidade Federal de Goiás
Resumo
samples and variables selection simultaneously. The algorithmic methods combination
for selecting samples and variables in the multivariate calibration aims to building an
effective model for predicting the concentration of a certain interest property.
As study case uses data acquired by a material analysis with near infrared waves (NIR)
on wheat samples in order to estimate the proteins concentration.
The algorithms for selection samples as the random number generator (RNG), KennardStone
(KS), sample set partitioning based on joint X and Y (SPXY) were used in conjunction
with successive projection algorithms (SPA) and partial least square algorithm
(PLS) for selection of variables in order to obtain results that can be used for comparison
basis with the proposed algorithm AGCD results obtained.
The presented results by samples selection algorithms (GNA, KS and SPXY) were too
close,butwhenusedtogetherwithvariableselectionalgorithms(SPAandPLS)theresults
were better in RMSEP terms.
TheAGCDachievedsignificantlybetterresultscomparedtotheotherstestedalgorithms,
reaching an improvement of 97% in comparison with the KS algorithm and an improvement
of 63% over SPXY-PLS algorithm, which further approached the AGCD results.
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SANTIAGO, Kelton de Sousa. Algoritmo evolutivo de cromossomo Duplo para calibração multivariada. 2013. 60 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2013.