STUDY OF THE POSSIBILITY OF USING MACHINE LEARNING METHODS TO PREDICT THE DEVELOPMENT OF BRANCHES OF SCIENCE

Статья посвящена определению принципа использования библиометрических показателей при создании подсистемы прогнозирования публикационной активности с использованием интеллектуальных методов. Показано, что наиболее оптимальным является метод машинного обучения DecisionTree. The article is devoted to determining the principle of using bibliometric indicators when creating a subsystem for predicting publication activity using intelligent methods. Key bibliometric indicators and examples of calculations for three areas within the technical scientific direction are presented: neural networks, genetic algorithms and data science. Machine learning methods for forecasting under uncertainty are analyzed. The choice of tools for the development of a mathematical model of the decision-making process in the field of scientific research has been made. It is shown that the DecisionTree machine learning method is the most optimal. The methodological apparatus is described, a training sample is created, and a predictive mathematical model is proposed that allows one to speak with sufficient accuracy about the number of future publications within each selected scientific group.