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We propose a stochastic optimization-based method to identify an optimal subset of measured variables for process monitoring.
A binary bare bones particle swarm optimization (BPSO) method is proposed to select an optimal subset of features [31].
The optimal subset of the basis function is selected with the global optimization algorithm; which can accurately represent the trend of true response surface.
Identification of a small optimal subset of CpG sites as bio-markers from high-throughput DNA methylation profiles.
Different feature selection algorithms were tested to select the optimal subset of features.
Genetic algorithm (GA), along with multiple linear regression (MLR) was employed to select the optimal subset of descriptors.
The optimal subset of TF features is selected using the wrapper method with sequential forward feature selection (SFFS).
Searching for the optimal subset of features is known as a challenging problem in feature selection process.
The genetic algorithm combined with the multiple linear regression (GA-MLR) was used to select optimal subset of descriptors which had significant contribution to the superheat limit temperature.
The genetic algorithm combined with partial least squares (PLS) was employed to select optimal subset of descriptors which had significant contribution to the overall heat of reaction.
The results analyses confirm that the models are able to determine the optimal subset of the influential variables that best predicts the chloride profile from the input dataset.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com