procedure for automatic models structure generation, which imitates the process of biological selection with consideration of pairwise successive features. Such procedure is currently used in deep learning networks. To compare and choose optimal models, two or more subsets of a data sample are used. This makes it possible to avoid preliminary assumptions because sample division implicitly acknowledges different types of uncertainty during the automatic construction of the optimal model.

During development