Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 Methods
Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 Methods
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DOI：
10.1109/icdmw53433.2021.00138
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Atharv Yeoleka;Sagar Patel;Shreejaa Talla;Krishna Rukmini Puthucode;Azim Ahmadzadeh;V. Sadykov;R. Angryk
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文献类型：
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作者：
Atharv Yeoleka;Sagar Patel;Shreejaa Talla;Krishna Rukmini Puthucode;Azim Ahmadzadeh;V. Sadykov;R. Angryk
太阳耀斑空间天气分析 (SWAN-SF) 是最近创建的多元时间序列基准数据集，旨在为太阳物理学界提供服务，作为太阳耀斑预测模型的测试平台。 SWAN-SF 包含 54 个独特特征，其中 24 个定量特征是根据活动区域的光球磁场图计算得出的，描述了其先前的耀斑活动。在这项研究中，我们首次系统地解决了量化这些特征与耀斑预报这一雄心勃勃的任务的相关性的问题。我们为预处理、特征选择和评估阶段实现了端到端管道。我们整合了 24 种特征子集选择 (FSS) 算法，包括多变量和单变量、监督和无监督、包装器和过滤器。我们在方法上比较了不同 FSS 算法在多变量时间序列和矢量化格式上的结果，并通过使用选定的特征以单变量和多变量方式对未见数据进行耀斑预测，尽可能测试其相关性和可靠性。我们根据最佳 FSS 方法的 top-k 特征和结果分析报告结束了我们的调查。我们希望我们的研究的可重复性和数据的可用性使未来的尝试能够与我们的研究结果和本身进行比较。
The Space-Weather ANalytics for Solar Flares (SWAN-SF) is a multivariate time series benchmark dataset recently created to serve the heliophysics community as a testbed for solar flare forecasting models. SWAN-SF contains 54 unique features, with 24 quantitative features computed from the photospheric magnetic field maps of active regions, describing their precedent flare activity. In this study, for the first time, we systematically attacked the problem of quantifying the relevance of these features to the ambitious task of flare forecasting. We implemented an end-to-end pipeline for preprocessing, feature selection, and evaluation phases. We incorporated 24 Feature Subset Selection (FSS) algorithms, including multivariate and univariate, supervised and unsupervised, wrappers and filters. We methodologically compared the results of different FSS algorithms, both on the multivariate time series and vectorized formats, and tested their correlation and reliability, to the extent possible, by using the selected features for flare forecasting on unseen data, in univariate and multivariate fashions. We concluded our investigation with a report of the best FSS methods in terms of their top-k features, and the analysis of the findings. We wish the reproducibility of our study and the availability of the data allow the future attempts be comparable with our findings and themselves.