Classification of Metageosystems by Ensembles of Machine Learning Models
Classification of Metageosystems by Ensembles of Machine Learning Models
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Stanislav Yamashkin, Anatoliy Yamashkin, Milan Radovanović, Marko Petrović, Ekaterina Yamashkina
|DOI : 10.14445/22315381/IJETT-V70I9P226|
How to Cite?
Stanislav Yamashkin, Anatoliy Yamashkin, Milan Radovanović, Marko Petrović, Ekaterina Yamashkina, "Classification of Metageosystems by Ensembles of Machine Learning Models" International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 258-268, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I9P226
The article describes geoinformation methods and algorithms for interpreting Earth remote sensing data based on forming an ensemble of shallow classifiers based on the Ensemble Learning methodology. The proposed solution can be used to assess the stability of geosystems and predict natural processes. The difference between the created approach is determined by the new organization scheme of the metaclassifier as a decision-making unit and the use of a geosystem approach to preparing data for automated analysis through deep learning models. The article shows that the use of ensembles built according to the proposed method makes it possible to carry out an automated operational analysis of spatial data for solving the problem of the thematic mapping of metageosystems and natural processes to provide conditions for the sustainable development of regions. At the same time, combining models into an ensemble based on the proposed architecture of the metaclassifier makes it possible to increase the stability of the analyzing system: the accuracy of decisions made by the ensemble tends to the accuracy of the most efficient monoclassifier of the system.
Machine learning, Deep learning, Artificial neural network, Spatial data, Ensembles, Sustainable development.
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