Abstract
The article is devoted to the development of mathematical models for the classi-fication of uncertain data represented by fuzzy values and certainty factors CF(A). Diagnostic pattern formation procedures use modified Hamming networks (MHN), as well as reduction methods and Cohen's kappa statistics. At the same time, the limit-ing dimensions and composition of the parameters of the classification model are de-termined, which ensure the established probabilistic requirements for the reliability of the calculation results. The model space reduction procedure and the structure of the software complex for diagnosing uncertain data are presented. An example of a clas-sification model based on fuzzy data is the task of identifying the authors of Ukrain-ian-language texts. The classification task for data in CF(A) format corresponds to candidate selection. The results of the numerical modeling made it possible to estab-lish the effectiveness, reliability and efficiency of the proposed procedures for the formation of reliable classification models with uncertain data.
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