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Cybernetics and programming
Reference:

Decision-Making Simulation Based on Multidimensional Data Analysis

Pesterev Egor Vasilievich

senior lecturer of the Department of Air Traffic Management at Saint Petersburg State University of Civil Aviation

196210, Russia, Saint Petersburg, str. Pilotov, 38, room No. 353a

pesegor@gmail.com
Klyushin Yaroslav Grigorievich

PhD in Physics and Mathematics

associate professor of the Department of Applied Mathematics at Saint Petersburg State University of Civil Aviation

196210, Russia, Saint Petersburg, str. Pilotov, 38, office No. 375

klyushin7748848@live.ru

DOI:

10.7256/2306-4196.2016.3.18956

Review date:

26-04-2016


Publish date:

25-06-2016


Abstract: Methods of multidimensional data processing in the decision making processes are an essential part of the business-processes analysis. In this research the authors intend to analyze multidimensional operations when numerous alternatives are presented. Thus, the subject of the research is the decision making process based on the analysis of multidimensional data received from the system functioning statistics. The authors suggest to analyze the subject of the research that is proposed in terms of statistical data processing from the point of view of the general approach, in particular, within the framework of the image discrimination theory and chemometrics. The authors of the article offer particular methods for generating and processing statistical data. These methods involve developing a multidimensional data structure followed by processing its production function (certainty function). The authors suggest to analyze oiriginal features constituting selected data from the point of view of the dominating motivation principle that is mathematically demonstrated as the mutual influence between these features as well as on the decision to be made. To verify the methods, the authors have performed a number of numerical experiments aimed at both comparing developed algorithms reflecing the authors' approach with Bayesian approach and comparing different production functions. A number of experiments intend to find out the arithmetical mean of several generated random numbers. As a result, the authors have proved the dependence of the number of correct responses on constitutive parameters (the number of objects, the number of features, the volume of the selected data, the number of possible values for each feature). The results of the research demonstrate the better practice of classifying objects based on the authors' methods compared to the classification based on the probability approach. The results can be used to solve a wide range of tasks that are not directly related to the decision making process but deal with multidimensional data analysis. 


Keywords: multiple-factor models, Bayesian probability, chemometrics, pattern recognition, multidimensional data, statistical data, production function, confidence function, decision support, multivariate statistics
This article written in Russian. You can find full text of article in Russian here .

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