用有序性信息增强二分类损失，提升太阳耀斑预测精度
Ordinal Encoding as a Regularizer in Binary Loss for Solar Flare Prediction
- 在传统二分类损失中引入子类有序性权重
- 靠近阈值的误判惩罚更重，提升边界识别能力
- 适合需要精细区分强弱事件的天文预测场景
太阳耀斑预测通常被建模为二分类任务，根据特定阈值（如C级、M级或X级）将事件划分为耀斑（FL）或无耀斑（NF）。然而，这种二分框架忽略了各类别内部子类之间的固有序列关系。已有研究表明，最常见的误判发生在预测阈值附近，表明模型难以区分强度相近但位于阈值两侧的事件。为此，本文提出一种改进的损失函数，将二值化标签中子类的有序信息融入传统的二元交叉熵（BCE）损失中。该方法作为感知有序性的数据驱动正则化策略，在模型优化过程中对靠近阈值的错误预测施加更大惩罚，从而强化模型对边界事件的区分能力。通过在损失函数中引入有序权重，旨在利用数据的有序特性提升学习效果，进而改善整体预测性能。
原文摘要 · Abstract (English)
The prediction of solar flares is typically formulated as a binary classification task, distinguishing events as either Flare (FL) or No-Flare (NF) according to a specified threshold (for example, greater than or equal to C-class, M-class, or X-class). However, this binary framework neglects the inherent ordinal relationships among the sub-classes contained within each category (FL and NF). Several studies on solar flare prediction have empirically shown that the most frequent misclassifications occur near this prediction threshold. This suggests that the models struggle to differentiate events that are similar in intensity but fall on opposite sides of the binary threshold. To mitigate this limitation, we propose a modified loss function that integrates the ordinal information among the sub-classes of the binarized flare labels into the conventional binary cross-entropy (BCE) loss. This approach serves as an ordinality-aware, data-driven regularization method that penalizes the incorrect predictions of flare events in close proximity to the prediction threshold more heavily than those away from the boundary during model optimization. By incorporating ordinal weighting into the loss function, we aim to enhance the model's learning process by leveraging the ordinal characteristics of the data, thereby improving its overall performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。