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Article title HIGH-EFFICIENCY METHOD AND ALGORITHMS OF AUTOMATIC CLASSIFICATION OF OBJECTS IN THE CONDITIONS OF PARAMETRICAL UNCERTAINTY AND CLASS INTERSECTION ON THE BASISOF METHODOLOGYWITH SYSTEM MAXIMIZATION OF ENTROPY
Authors A.V. Getmanchuk
Section SECTION I. DATA ANALYSIS AND KNOWLEDGE MANAGEMENT
Month, Year 07, 2016 @en
Index UDC
DOI DOI 10.18522/2311-3103-2016-7-3952
Abstract In the report the problem of objects classification in the conditions of parametrical uncer-tainty and class intersection is considered. Specificity of the problem is absence of reliable infor-mation about parameters of a priori known classes presented in the catalogue of reference values in the form of confidence intervals of signs. Such kind of uncertainty imposes essential restrictions on a range of methods and algorithms which can be used for the solution of the problem. In the report G.V. Sheleykhovskiy"s method of objects classification, which takes into account required restrictions and possessing high degree of reliability of classification is considered. The basic imperfections of the method complicating its application in real conditions are shown. On the basis of G.V. Sheleykhovskiy"s method the combined method of the classification possessing improved productivity and devoid of known imperfections of the basic method is developed. The combined method assumes improvement of the base G.V. Sheleykhovskiy"s method by the relative positioning analysis in parametrical space of the classification objects presented in the form of points and a priori known classes from the catalogue of reference values, representing areas in the parametrical space. In the document the following algorithms realising the developed method are presented: algorithm of the entrance data preliminary analysis, algorithm of the classification matrix reduction, algorithm of list representation of the classification matrix, algorithm of classification results analysis and formation of information data of new objects. The developed method allows to reduce considerably computing labouriousness of a base method, and also to relieve it of a number of essential defects. So, for example, the known problem of G. V. Sheleykhovskiy’s method convergence is solved. In article existence of specific objects which influence process of convergence of this method is proved. The offered way of a solution of a convergence problem of a base method of classification differs from known ways that it does not demand increase in expenses of computing and time resources. In work the performed experimental researches are described and the received results are presented. The present results allow to draw a conclusion on decrease in the general of computing labouriousness at classification of objects with use of the combined method in relation to the method of classification of G.V. Sheleykhovskiy on the average twice.

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Keywords Genetic algorithm; clustering; information retrieval; prediction.
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