Design of the universal classifier as a RBF network based on the CART solution tree
The aim of the paper was to develop a universal classifier in the form of a radial basis function network (RBF network) based on the Gaussian function and the CART Solution Tree. The examples of diseases diagnostics classifier were considered. It is noted that during the classifier development, it is necessary to determine the number of RBF neurons and the values of parameters of these neurons (centre, dispersion). For this purpose, a method that allows splitting the space of features into relatively homogeneous domains in the form of hyperparallelepipeds, each of which is associated with one of the RBF neurons, is proposed. The number of RBF neurons and parameters of these neurons are determined automatically directly based on the CART Solution Tree. As a result of the research, it was found that the proposed classifiers show the highest efficiency on the learning set with a minimal Solution Tree reduction (accuracy from 80 % to 95 %). It was shown that for two and more classes the accuracy of these classifiers on the test set makes 79 % and more, however, provided that the appropriate data sample for the learning set is selected. The possibility of using the RBF network based on the Gaussian function and the CART Solution Tree in the healthcare system for the diseases diagnostics and medical systems (or devices) assistance during decision-making support was proved. The obtained results could be further applied to improve the universal classifier development method based on the RBF network