ASC-ANALYSIS OF WINE CLASSES DUE TO THEIR PROPERTIES BASED ON DATA FROM THE UCI REPOSITORY
Creation of artificial intelligence systems is one of important and perspective directions of development of modern information technology. As there are many alternatives to artificial intelligence systems, there is a need to evaluate mathematical models of these systems. In this work, we present a solution of the problem of identifying classes of salary levels of employees depending on their characteristics. To achieve this goal it requires free access to test the source data and methodology, which will help to convert the data into the form needed for work in artificial intelligence systems. A good choice is a database of test problems for systems of artificial intelligence of UCI repository. In this work we used the database called "Wine Data Set" from the Bank's original task of artificial intelligence from repository UCI. The most reliable in this application was the model of the INF4 based on semantic, according to A. Kharkevich, integral criteria of "Amount of knowledge". The accuracy of the model is 0,916, which is much higher than the reliability of expert evaluations, which is equal to about 70%. To assess the reliability of the models in the ASC-analysis and the system of "Eidos" we used the F-criterion of van Ritbergen and fuzzy multiCLASS generalization proposed by Professor E. V. Lutsenko (L-measure)