Data Mining of High Accuracy for the Efficiency in the Task of Massive Printing
Source: Dongseo University
Random forests are known to be robust for missing and erroneous data as well as irrelevant features. Moreover, even though the forests have many trees, they can utilize the fast building property of decision trees, so they do not require much computing time. In this paper an efficient procedure that utilizes random forests to predict the cylinder bands in rotogravure printing is shown. Even though several research results have been published already to find better prediction accuracy based on other methods, a new and very good result has been found with the suggested method having appropriate parameters of random forests.