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Machine learning techniques for classifying dangerous asteroids
There is an infinite number of objects in outer space, and these objects and asteroids might be harmful. Hence, it is wise to know what is surrounding us and what can harm us amongst those. Therefore, in this article, with the hyperparameters tuning of Extra Tree, Random Forest, Light Gradient Boost...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10480302/ https://www.ncbi.nlm.nih.gov/pubmed/37680366 http://dx.doi.org/10.1016/j.mex.2023.102337 |
Sumario: | There is an infinite number of objects in outer space, and these objects and asteroids might be harmful. Hence, it is wise to know what is surrounding us and what can harm us amongst those. Therefore, in this article, with the hyperparameters tuning of Extra Tree, Random Forest, Light Gradient Boosting Machine, Gradient Boosting, and Ada Boost, the hazards of asteroids around the Earth were classified, and the results of ROC Curves for these algorithms were compared. • Reviewing the list of NASA-certified asteroids classified as the nearest Earth object; • Investigating the risk of asteroids with the help of Extra Tree, Random Forest, Light Gradient Boosting Machine, Gradient Boosting, and Ada Boost; • Comparing the performance of machine learning algorithms in the classification of high-risk asteroids. |
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