A consistent approach for the inherently imbalanced problem of solar energetic particle (SEP) events binary prediction is being presented. This is based on solar flare and coronal mass ejection (CME) data and combinations of both thereof. We exploit several machine learning (ML) and conventional statistics techniques to predict SEPs. The methods used are logistic regression (LR), support vector machines (SVM), neural networks (NN) in the fully connected multi-layer perceptron (MLP) implementation, random forests (RF), decision trees (DTs), extremely randomized trees (XT) and extreme gradient boosting (XGB). We provide an assessment of the methods employed and conclude that RF could be the prediction technique of choice for an optimal sample comprised by both flares and CMEs. The best-performing method gives a Probability of Detection (POD) of 0.76(±0.06), False Alarm Rate (FAR) of 0.34(±0.10), true skill statistic (TSS) 0.75(±0.05), and Heidke skill score (HSS) 0.69(±0.04). We further show that the most important features for the identification of SEPs, in our sample, are the CME speed, width and flare soft X-ray (SXR) fluence.

正在提出一种解决太阳高能粒子 (SEP) 事件二元预测的固有不平衡问题的一致方法。这是基于太阳耀斑和日冕物质抛射 (CME) 数据以及两者的组合。我们利用多种机器学习 (ML) 和传统统计技术来预测 SEP。使用的方法有逻辑回归 (LR)、支持向量机 (SVM)、全连接多层感知器 (MLP) 实现中的神经网络 (NN)、随机森林 (RF)、决策树 (DT)、极度随机化树(XT) 和极端梯度提升 (XGB)。我们对所采用的方法进行了评估，并得出结论，RF 可能是由耀斑和 CME 组成的最佳样本的首选预测技术。性能最佳的方法的检测概率 (POD) 为 0。76(±0.06)，误报率 (FAR) 为 0.34(±0.10)，真实技能统计 (TSS) 0.75(±0.05)，海德克技能得分 (HSS) 0.69(±0.04)。我们进一步表明，在我们的样本中，识别 SEP 的最重要特征是 CME 速度、宽度和耀斑软 X 射线 (SXR) 能量密度。