A Software Implementation and Research of Ensemble Algorithms for Solving the Different Types of Data Classification Problem

Many algorithms for solving classification problems have been studied and developed at the moment, but the problem of improving the quality (accuracy, stability) of classification solutions remains relevant. This problem can be solved by developing ensemble classification algorithms, as well as by performing a qualitative primary analysis and processing of the initial data. This research reports the results of research and implementation of ensemble algorithms, which are being developed in software to solve the problem of classification of different types of data. Homogeneous and heterogeneous ensemble classification models were implemented by using R language. Several classifiers were built on the basis of real agricultural data using the developed models. Comparative testing showed the advantages of using ensemble algorithms compared to single classifiers (increased classification accuracy, reduced logarithmic losses). According to the results of the study, the conclusion about the prospects of development and application of ensemble models to solve the problem of classification and software to solve the problems of analysis and data processing in various fields using modern technologies and software tools.