Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/40287
metadata.artigo.dc.title: Machine learning based on extended generalized linear model applied in mixture experiments
metadata.artigo.dc.creator: Liska, Gilberto Rodrigues
Cirillo, Marcelo Ângelo
Menezes, Fortunato Silva de
Bueno Filho, Julio Silvio de Sousa
metadata.artigo.dc.subject: Dispersion model
Boosting algorithm
Machine learning
Regression modeling
Simplex space
Modelo de dispersão
Algoritmo de reforço
Aprendizado de máquina
Modelos de regressão
Experimentos de mistura
metadata.artigo.dc.publisher: Taylor & Francis Group
metadata.artigo.dc.date.issued: 2019
metadata.artigo.dc.identifier.citation: LISKA, G. R. et al. Machine learning based on extended generalized linear model applied in mixture experiments. Communications in Statistics - Simulation and Computation, London, 2019. DOI: 10.1080/03610918.2019.1697821
metadata.artigo.dc.description.abstract: When performing mixture experiments, we observe that maximum likelihood methods present problems related to the collinearity, small sample size, and over/under dispersion. In order to overcome these problems, this investigation proposes a model built in accordance with a machine learning approach. This approach will be called Boosted Simplex Regression, which has been evaluated both in terms of accuracy and precision for the odds ratio. The advantages of this new approach are illustrated in a mixture experiment, which has made us conclude that the model Boosted Simplex Regression has unveiled not only better fit quality but also more precise odds ratio confidence intervals.
metadata.artigo.dc.identifier.uri: https://www.tandfonline.com/doi/full/10.1080/03610918.2019.1697821
http://repositorio.ufla.br/jspui/handle/1/40287
metadata.artigo.dc.language: en
Appears in Collections:DES - Artigos publicados em periódicos
DFI - Artigos publicados em periódicos

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