闪烁
机器学习
梯度升压
计算机科学
人工智能
特征(语言学)
特征选择
行星际闪烁
Boosting(机器学习)
排名(信息检索)
太阳风
物理
探测器
电信
随机森林
语言学
哲学
日冕物质抛射
量子力学
磁场
作者
Alexis J. Wu,Yunxiang Liu
摘要
This paper introduces a machine learning approach to investigate the feature importance for scintillation forecasting. Here, the features are historical measurements used as input for machine learning models. We propose to use gradient boosting as the machine learning algorithm to conduct a scintillation forecasting task at high latitudes. Once the gradient boosting model is trained, the rank of feature importance can be obtained. The preliminary results show that the top 10 most important features indeed are correlated with the future occurrence of scintillation. The feature importance ranking has the potential to guide feature selection for machine learning-based scintillation forecasting and improve forecasting performance. In addition, the feature importance list could also provide insights on the investigation of the complex coupling between solar wind and ionospheric disturbance.
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