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Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%‐80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique...
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