The implementation of the neural networks to the problem of economic classification of countries

Abstract

This paper shows practical implementation of the multilayer feedforward neural network, trained by supervised backpropagation algorithm, to the problem of automatic classification of countries into beforehand predefined categories of economic development, contained in the United Nations report entitled World Economic Situation and Prospects 2012. The goal of the paper is to automate the process of classification of countries, to define a set of key measurable economic development indicators, as well as to emphasize significance of neural networks for solving classification problems. The research includes classification of 168 countries in 4 groups of economic development, based on 7 selected measurable indicators. The data from the official reports of the international economic institutions served for training of the intelligent decision-making system based on neural network, and as a measure of quality of training, confusion matrix was used, showing the precision of the intelligent system by determining the percentage of overlap with empirically obtained data. Precision of automatic classification speaks of neural networks as powerful apparatus for solving classification problems, but also of justification of choice of classification parameters and their importance. The importance of selected indicators is reflected in the fact that knowledge of their value is sufficient condition for automatic classification with reliability level of 80%.