Solar flare is one of the violent solar eruptive phenomena; many solar flare forecasting models are built based on the properties of active regions. However, most of these models only focus on active regions within 30° of solar disk center because of the projection effect. Using cost sensitive decision tree algorithm, we build two solar flare forecasting models from the active regions within 30° of solar disk center and outside 30° of solar disk center, respectively. The performances of these two models are compared and analyzed. Merging these two models into a single one, we obtain a full-disk solar flare forecasting model.

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基于数据挖掘方法的全盘太阳耀斑预测模型

太阳耀斑是强烈的太阳喷发现象之一。基于活跃区域的属性，建立了许多太阳耀斑预测模型。但是，由于投影效果，这些模型中的大多数仅聚焦在太阳盘中心30°以内的活动区域。使用成本敏感决策树算法，我们分别从太阳盘中心30°内和太阳盘中心30°外的活动区域建立了两个太阳耀斑预测模型。比较和分析了这两种模型的性能。将这两个模型合并为一个模型，我们可以获得全盘太阳耀斑预测模型。