Otimização multiobjetivo para seleção simultânea de variáveis e objetos em cromossomo duplo de representação inteira para calibração multivariada
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2017-08-24
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Universidade Federal de Goiás
Resumo
Multiobjective Optimization for feature and samples selection in double chromosome of integer
representation and variable size for multivariate calibration}
In several problems of regression, classification, prediction, approximation
Optimization, the original data contain a large number of variables to obtain a better representation of the
problem under consideration. However, a significant part of the variables may be irrelevant and redundant
from the point of view of machine learning. Indeed, one of the challenges to be overcome is a selection of a
subset of variables that has the best perform. One of the breakthroughs in this type of problem is the use of a
multiobjective formulation that avoids the overlap of the model to the training data set. Another important
point is the process of choosing the objects to be used in the learning stage. Generally, a selection of
variables and treatment objects are treated separately and without dependence. This project proposes a
multiobjective modeling to select variables and objects simultaneously using a genetic integer representation
algorithm with variable size chromosomes. It is expected that a simultaneous selection of objects and
variables on a multiobjective context produce better results in a traditional approach. As a case study this
work utilized an analysis of near infrared (NIR) material on oil samples for the purpose of estimating the
concentration of an interest properties such set was used in the competition conducted at the International
Diffuse Reflectance Conference (IDRC) in the year 2014.
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BASTOS, H. K. O. Otimização multiobjetivo para seleção simultânea de variáveis e objetos em cromossomo duplo de representação inteira para calibração multivariada. 2017. 76 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2017.