Algorithms for generating training sets in a system with case-based inference based on example situations

The paper considers the issue of creating training sets and their scaling in machine learning problems. The subject of the study is the process of generating training sets based on examples in order to augment them. To implement the idea of expansion, it is proposed to use the transformation of existing examples of situations. The transformation of examples is based on a well-known optimization method - the method of coordinate descent. The paper describes the statement of the problem of transformations of example situations in terms of the introduced representation model. There are proposed algorithms that make it possible to obtain an extended set from the initial set of example situations specified using formal representations, which will include situations that meet the similarity criteria with these examples. The paper presents the testing of the proposed algorithms for expanding a set of example situations, carried out in order to form a data set for the studying artificial neural networks. The obtained results are of practical importance for training artificial neural networks used in intelligent decision support systems. The proposed algorithms make it possible to automate the formation of datasets using the available prepared and approved examples of typical situations and solving the transformation problem as the problem of finding the optimum of the similarity objective function.