Abstract
Managers of manufacturing systems are constantly looking for ways to predict future production conditions. Due to the system’s complexity, modelling is effortful and never completed. So-called random forests of decision trees seem to be a promising machine-learning tool to forecast key figures of manufacturing systems. The selection of data to teach such classifiers significantly influences the quality of the prediction. However, quality of data and prediction decreases in case of a dynamic system. This paper deals with one possible way of data handling for decision forests in changing manufacturing system.
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