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This is an "11 factor-2 level-12 run" Plackett-Burman experimental design dataset.
However, mathematical programming formulations that minimize the number of misclassifications on the design dataset suffer from computational difficulties.
The basic idea is to improve an LP-generated classifier with respect to the number of misclassifications on the design dataset.
We present mathematical programming based heuristics for finding classifiers with a small number of misclassifications on the design dataset with multiple classes.
We first train a Bayesian network to inherently encode the dependency between decorative styles and furniture colours, as well as the relevancy between furniture colours themselves, using a real interior design dataset.
Laboratory research comprises multiple activities including experimental design, dataset construction, hypothesis formation and hypothesis testing.
Similar(50)
Related studies are compared by their classifier design, datasets used, and other experimental setups.
For each design, datasets were simulated in 10 scenarios with different proportions of up- and down-regulated genes in the set, and either independent or correlated expression profiles.
The goal of the current study was to validate, compare, and optimize these methodological approaches by applying them to an olfactory fMRI block-design dataset from 10 healthy young subjects presented with odorants for 12 s (ON), alternating with 30 s of clear air (OFF).
The designed dataset is used for training and tuning of the rule base while using GA as the tuning algorithm.
However, we did not test this hypothesis with a specifically designed dataset because it has never been seriously disputed [ 1].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com