Algoritmo evolutivo multi-objetivo em tabelas para seleção de variáveis em classificação multivariada
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2014-10-29
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Universidade Federal de Goiás
Resumo
This work proposes the use of multi-objective evolutionary algorithm on tables (AEMT)
for variable selection in classification problems, using linear discriminant analysis. The
proposed algorithm aims to find minimal subsets of the original variables, robust classifiers
that model without significant loss in classification ability. The results of the classifiers
modeled by the solutions found by this algorithm are compared in this work to
those found by mono-objective formulations (such as PLS, APS and own implementations
of a Simple Genetic Algorithm) and multi-objective formulations (such as the simple
genetic algorithm multi -objective - MULTI-GA - and the NSGA II). As a case study,
the algorithm was applied in the selection of spectral variables for classification by linear
discriminant analysis (LDA) of samples of biodiesel / diesel. The results showed that the
evolutionary formulations are solutions with a smaller number of variables (on average)
and a better error rate (average) and compared to the PLS APS. The formulation of the
AEMT proposal with the fitness functions: medium risk classification, number of selected
variables and number of correlated variables in the model, found solutions with a lower
average errors found by the NSGA II and the MULTI-GA, and also a smaller number of
variables compared to the multi-GA. Regarding the sensitivity to noise the solution found
by AEMT was less sensitive than other formulations compared, showing that the AEMT
is more robust classifiers. Finally shows the separation regions of classes, based on the
dispersion of samples, depending on the selected one of the solutions AEMT, it is noted
that it is possible to determine variables of regions split from the selected variables.
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Citação
RIBEIRO, L. A. Algoritmo evolutivo multi-objetivo em tabelas para seleção de variáveis em classificação multivariada. 2014. 84 f. Dissertação (Programa de Pós-graduação em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2014.