Application of information technology for the analysis of the rating of university

This paper builds a model of predicting the rating of the University on the basis of a neural network in IBM SPSS Statistics. The choice is due to the fact that the program contains gradient descent error function, which is able to automatically configure the network for data classification. The authors describe the modeling technique, a step-by-step algorithm for selecting the architecture of the network, setting its parameters, training and testing. Experiment data of 1102 Russian universities and 123 indicators of their activity was used for this experiment. A vector was supplied as an input for the network, the coordinates of which were the average total score of each University. Indicators were considered independent variables. 30 out of 123 indicators were left for the study by the method of correlation analysis. The number of input neurons was equal to the number of independent variables. The output layer contained the amount of neurons equal to the number of dependent variables. The activation function of neurons in the hidden and output layer is sigmoid. The authors present the results of modeling. Using the constructed model, the input data was divided into clusters: "efficient", "inefficient". Centers of clusters were determined. The sample was split for two network architectures with different number of layers and neurons. The percentage of error on the control and training samples was calculated. Quality of the proposed model was evaluated using ROC (Receiver Operating Characteristic) curve. © 2018 CEUR-WS. All Rights Reserved.