Machine learning techniques for classifying dangerous asteroids

被引：1

作者：

Malakouti, Seyed Matin ^{[1
]}

Menhaj, Mohammad Bagher ^{[1
]}

Suratgar, Amir Abolfazl ^{[1
]}

机构：

[1] Amirkabir Univ Technol, Dept Elect Engn, Distributed & Intelligent Optimizat Res Lab, Tehran, Iran

来源：

METHODSX | 2023年 / 11卷

关键词：

Asteroids; NASA; Hazards of asteroids; Machine learning; CLASSIFICATION;

D O I：

10.1016/j.mex.2023.102337

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.& BULL; Reviewing the list of NASA-certified asteroids classified as the nearest Earth object & BULL; Investigating the risk of asteroids with the help of Extra Tree, Random Forest, Light Gradient Boosting Machine, Gradient Boosting, and Ada Boost & BULL; Comparing the performance of machine learning algorithms in the classification of high-risk asteroids

引用

页数：8

共 32 条

[1] Light Gradient Boosting Machine for General Sentiment Classification on Short Texts: A Comparative Evaluation [J].