Fast Counterfactual Explanation for Solar Flare Prediction
Fast Counterfactual Explanation for Solar Flare Prediction
复制标题
DOI：
10.1109/icmla55696.2022.00199
复制
发表时间：
2022-12
期刊：
影响因子：
--
通讯作者：
Peiyu Li;O. Bahri;S. F. Boubrahimi;S. M. Hamdi
中科院分区：
文献类型：
--
作者：
Peiyu Li;O. Bahri;S. F. Boubrahimi;S. M. Hamdi
太阳耀斑预测由于其潜在的不利空间天气影响而成为空间天气研究的重要内容。近年来，人们提出了一套用于太阳耀斑预测的机器学习模型，并在之前的技术水平上进行了重大改进。然而，现有的研究大多集中在预测任务上，而忽略了预测任务背后的可解释性。本文提出了一种基于太阳耀斑预测的事后解释方法FAST-CF。特别地，我们引入了最近的不像邻居来指导反事实搜索，从而快速搜索到最优结果。此外，FAST-CF包含了太阳耀斑预测的反事实解释的理想特性。我们使用不同的评估指标来比较FAST-CF与其他两个基线的性能，并验证我们的方法相对于现有技术的优越性。
Solar flare prediction has become essential in space weather research due to its potential adverse space-weather ramifications. Over recent years, a set of machine learning models on solar flare prediction have been proposed and significant improvement has been made over the previous state of the art. However, most existing research work focuses on the prediction task and ignores the interpretability behind the prediction task. In this paper, we provide a post-hoc explanation method based on solar flare prediction, FAST-CF. In particular, we incorporate the nearest unlike neighbor for guiding the counterfactual search, which is fast to search for the optimal result. In addition, FAST-CF encapsulates the desirable properties of a counterfactual explanation for solar flare prediction. We use different evaluation metrics to compare the performance of FAST-CF with the other two baselines and verify the superiority of our method to existing state-of-the-art.