Use este identificador para citar ou linkar para este item: http://repositorio.bc.ufg.br/handle/ri/14796
Tipo do documento: Artigo
Título: Genetic algorithm for variable and samples selection in multivariate calibration problems
Autor: Santiago, Kelton de Souza
Soares, Anderson Silva
Lima, Telma Woerle de
Coelho, Clarimar José
Gabriel, Paulo Henrique Ribeiro
Abstract: One of the main problems of quantitative analytical chemistry is to estimate the concentration of one or more species from the values of certain physicochemical properties of the system of interest. For this it is necessary to construct a calibration model, i.e., to determine the relationship between measured properties and concentrations. The multivariate calibration is one of the most successful combinations of statistical methods to chemical data, both in analytical chemistry and in theoretical chemistry. Among used methods can cite Artificial Neural Networks (ANN), the Nonlinear Partial Least Squares (N-PLS), Principal Components Regression (PCR) and Multiple Linear Regression (MLR). In addition of multivariate calibration methods algorithms of samples selection are used. These algorithms choose a subset of samples to be used in training set covering adequately the space of the samples. In other hand, a large spectrum of a sample is typically measured by modern scanning instruments generating hundreds of variables. Search algorithms have been used to identify variables which contribute useful information about the dependent variable in the model. This paper proposes a Genetic Algorithm based on Double Chromosome (GADC) to do these tasks simultaneously, the sample and variable selection. The obtained results were compared with the well-known algorithms for samples and variable selection Kennard-Stone, Partial Least Square and Successive Projection Algorithm. We showed that the proposed algorithm can obtain better calibrations models in a case study involving the determination of content protein in wheat samples.
Palavras-chave: Genetic algorithm
Variable selection
Regression
País: Outros
Unidade acadêmica: Instituto de Informática - INF (RG)
Citação: SANTIAGO, Kelton de Souza; SOARES, Anderson Silva; LIMA, Telma Woerle de; COELHO, Clarimar José; GABRIEL, Paulo Henrique Ribeiro. Genetic algorithm for variable and samples selection in multivariate calibration problems. Journal of Computer Sciences, Dubai, v. 11, n. 4, p. 621-626, 2015.
Tipo de acesso: Acesso Aberto
Identificador do documento: 10.3844/jcssp.2015.621.626
Identificador do documento: 10.3844/jcssp.2015.621.626
URI: http://repositorio.bc.ufg.br/handle/ri/14796
Data de publicação: 2015
Aparece nas coleções:INF - Artigos publicados em periódicos

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