提出多分类分数导向损失，直接优化评估指标，无需事后调阈值。
The Multiclass Score-Oriented Loss (MultiSOL) on the Simplex
- 将分数导向损失扩展到多分类，基于多重阈值框架设计新损失函数。
- 在多个数据集上表现媲美主流损失，对类别不平衡有较强鲁棒性。
- 适合关注评估指标直接优化与模型泛化能力的研究者。
在监督二分类中，分数导向损失通过将决策阈值视为具有先验分布的随机变量，实现训练阶段直接优化特定性能指标，避免事后阈值调整。本文利用近期提出的多维阈值分类框架，将此类损失推广至多分类场景，定义了多分类分数导向损失（MultiSOL）函数。实验表明，该损失家族保留了二分类中的主要优势，包括目标指标的直接优化和对类别不平衡的鲁棒性，在多个分类任务中性能可与当前最先进的损失函数相媲美，并为单纯形几何与分数导向学习之间的相互作用提供了新见解。
原文摘要 · Abstract (English)
In the supervised binary classification setting, score-oriented losses have been introduced with the aim of optimizing a chosen performance metric directly during the training phase, thus avoiding \textit{a posteriori} threshold tuning. To do this, in their construction, the decision threshold is treated as a random variable provided with a certain \textit{a priori} distribution. In this paper, we use a recently introduced multidimensional threshold-based classification framework to extend such score-oriented losses to multiclass classification, defining the Multiclass Score-Oriented Loss (MultiSOL) functions. As also demonstrated by several classification experiments, this proposed family of losses is designed to preserve the main advantages observed in the binary setting, such as the direct optimization of the target metric and the robustness to class imbalance, achieving performance comparable to other state-of-the-art loss functions and providing new insights into the interaction between simplex geometry and score-oriented learning.
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