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Subset selection from large datasets for Kriging modeling
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DOI：10.1007/s00158-008-0306-8.png)
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When building a Kriging model, the general intuition is that using more data will always result in a better model. However, we show that when we have a large non-uniform dataset, using a uniform subset can have several advantages. Reducing the time necessary to fit the model, avoiding numerical inaccuracies and improving the robustness with respect to errors in the output data are some aspects which can be improved by using a uniform subset. We furthermore describe several new and current methods for selecting a uniform subset. These methods are tested and compared on several artificial datasets and one real life dataset. The comparison shows how the selected subsets affect different aspects of the resulting Kriging model. As none of the subset selection methods performs best on all criteria, the best method to choose depends on how the different aspects are valued. The comparison made in this paper can be used to facilitate the user in making a good choice.
Keyword:
Design of computer experiments
Dispersion problem
Kriging model
Large non-uniform datasets
Radial basis functions
Robustness
Space filling
Subset selection
Uniformity
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4
论文数:
4.9K
被引数:
1.7W
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