СТЕКІНГ НЕЙРОПОДІБНОЇ СТРУКТУРИ МПГП З RBF ШАРОМ НА ПІДСТАВІ ГЕНЕРУВАННЯ ВИПАДКОВОГО КОРТЕЖУ ЇЇ ГІПЕРПАРАМЕТРІВ ДЛЯ ЗАВДАНЬ ПРОГНОЗУВАННЯ

Abstract

Improving prediction accuracy by artificial intelligence tools is an important task in various industries, economics, medicine. Ensemble learning is one of the possible options to solve this task. In particular, the construction of stacking models based on different machine learning methods, or using different parts of the existing data set demonstrates high prediction accuracy of the. However, the need for proper selection of ensemble members, their optimal parameters, etc., necessitates large time costs for the construction of such models. This paper proposes a slightly different approach to building a simple but effective ensemble method. The authors developed a new model of stacking of nonlinear SGTM neural-like structures, which is based on the use of only one type of ANN as an element base of the ensemble and the use of the same training sample for all members of the ensemble. This approach provides a number of advantages over the procedures for building ensembles based on different machine learning methods, at least in the direction of selecting the optimal parameters for each of them. In our case, a tuple of random hyperparameters for each individual member of the ensemble was used as the basis of ensemble. That is, the training of each combined SGTM neural-like structure with an additional RBF layer, as a separate member of the ensemble occurs using different, randomly selected values of RBF centers and centersfof mass. This provides the necessary variety of ensemble elements. Experimental studies on the effectiveness of the developed ensemble were conducted using a real data set. The task is to predict the amount of health insurance costs based on a number of independent attributes. The optimal number of ensemble members is determined experimentally, which provides the highest prediction accuracy. The results of the work of the developed ensemble are compared with the existing methods of this class. The highest prediction accuracy of the developed ensemble at satisfactory duration of procedure of its training is established.