Determining the Significance of Input Features in Predicting Magnetic Storms Using Machine Learning Methods

Авторы: Vladimirov Roman, Shirokiy Vladimir, Barinov Oleg, Myagkova Irina
Сборник: Advances in Neural Computation, Machine Learning, and Cognitive Research VII
Серия: Studies in Computational Intelligence
Том: 1120
Год издания: 2023
Издательство: Springer Nature
Местоположение издательства: Switzerland
Первая страница: 370
Последняя страница: 379
DOI: 10.1007/978-3-031-44865-2_40
Аннотация: In this paper, we study an algorithm for obtaining the most efficient model for predicting the amplitude of the geomagnetic Dst index, based on lowering the input data dimension by gradually discarding input features. This task is relevant, since the selection of significant input data is necessary for the effective use of machine learning methods. The study was carried out on the basis of the following machine learning methods: artificial neural network of the multilayer perceptron type, gradient boosting, linear regression. Comparison of the effectiveness of the listed methods is carried out.
Добавил в систему: Доленко Сергей Анатольевич

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