The problem of computer diagnostics of complex systems is one of the non-trivial tasks of modern information technology. Such systems are, for example, computer networks, automatic and/or automated control systems for complex technological objects, including related to complex problems of environmental protection, biology, etc. In pattern recognition, one of the major problems is forming subspaces of informative features, which only in the "ensemble" allow diagnosing the states of such systems with a high degree of reliability. An effective approach to solving this problem based on the principles of inductive modeling of complex systems is proposed. The quality criterion for recognizing classes of patterns is formulated, which also makes it possible to evaluate the quality of the constructed ensemble of informative features. As an example, the problem of constructing an ensemble of informative features represented by a binary code based on the data of an experiment to determine the hazard levels of some plant protection products is considered. Real primary data on plant protection products used in practice were applied to recognize the effect of certain characteristics on the so-called integrated "hazard indicator". Comparative numerical estimates of the effectiveness of the proposed approach are given. In this case, there can be a fivefold gain in the amount of computations for a relatively small number of input features equal to 5 compared to the known algorithms of the class considered in the paper. It is shown that, from a practical point of view, the described algorithm has advantages over the known algorithms with brute-force search of feature subspaces in pattern recognition problems.

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• Осипенко Валентина Петрівна (біологічні науки)
• Кулик Тетяна Володимирівна (фізико-математичні науки)

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