Abstract
The problem of improving the quality indicators of data processing models by segmenting data samples is considered. A multi-level data processing architecture is proposed, which allows determining the current properties of data in segments and assigning the best models according to the achieved quality indicators. A formal description of the architecture is given. The proposed solution is aimed at reducing the cost of retraining models in the case of transformation of data properties. Experimental studies have been carried out on a number of data sets that show an increase in the quality of processing indicators. The model can be considered as an improvement of ensemble methods of processing data samples.
Published Version
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