Y.E. Shishkin1,2, A.V. Skatkov1
1 Federal State Educational Institution of Higher Education «Sevastopol State University», Russian Federation, Sevastopol, Universitetskaya St., 33
2 Institute of Natural and Technical Systems, Russian Federation, Sevastopol, Lenin St., 28
The article analyzes the influence of the decision-making strategy of threshold values choice on the risk of the first and second kind errors in identifying anomalies caused by a shift in observation and an increase in dispersion. We consider the processes of monitoring the parameters of environment objects and processes, which in practice are described by general form distributions. In the course of a purposeful experiment, on a simulator stand, the anomalies of two classes were considered: a shift in observation and an increase in dispersion, the sensitivity of the statistical detection method was also estimated. The proposed approach makes it possible to determine how much the reliability of the determining anomalies function changes when the distribution law of the input parameter changes from normal to general distribution, in particular, exponential, Weibull and gamma distributions are considered.
A mathematical model has been developed and numerical experiments have been carried out which allow one to take into account errors of the first and second kind, with different values of shear and dispersion parameters. The application of the developed model allows obtaining updated estimates of the confidence interval for various distribution laws, which increases the reliability of the anomaly identification system, thus a significant improvement of the quality of decision making is achieved by taking measures to compensate for these errors, resulting in increased decision making reliability.
Practical recommendations on the choice of the decision support system thresholds for identification of anomalies, taking into account the distribution of the observed variable are formulated.
Keywords: monitoring, mathematical modeling, anomaly detection, clustering, critical systems, data mining, general distribution.
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