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Deep feature covering is a specialized framework in rough set theory used for constructing classifiers by hierarchical approximation of multi-layered data features. While distinct from common neural network deep features, it applies to complex, multi-layered data structures for improved decision-making. Further information on this construction method can be found in a detailed study available at Biblioteka Nauki . Deep Feature - an overview | ScienceDirect Topics