用多维阈值优化多分类预测，提升模型精度
Multiclass threshold-based classification
- 将softmax输出从概率解释转为单纯形上的几何判断
- 通过调整多维阈值，使各类网络预测性能持续提升
- 适合需要精细调优分类结果的研究者和工程应用
本文提出一种基于阈值的多分类框架，推广了标准的argmax规则。该方法将softmax输出的统计解释替换为在多维单纯形上的几何解释，分类决策依赖于多维阈值。这一视角转变使得任何训练好的分类网络均可通过后验阈值调节来优化分类得分，如同二分类场景中的常规操作。该机制支持对任意网络进行预测能力的进一步优化。此外，这种多维阈值设置允许定义基于阈值作为随机变量的得分导向损失函数。实验表明，多维阈值调优在多种网络与数据集上均带来一致的性能提升，所提出的多分类得分导向损失函数表现优于标准损失函数，展现出与二分类类似的优势。
原文摘要 · Abstract (English)
In this paper, we introduce a threshold-based framework for multiclass classification that generalizes the standard argmax rule. This is done by replacing the probabilistic interpretation of softmax outputs with a geometric one on the multidimensional simplex, where the classification depends on a multidimensional threshold. This change of perspective enables for any trained classification network an a posteriori optimization of the classification score by means of threshold tuning, as usually carried out in the binary setting. This allows a further refinement of the prediction capability of any network. Moreover, this multidimensional threshold-based setting makes it possible to define score-oriented losses, which are based on the interpretation of the threshold as a random variable. Our experiments show that the multidimensional threshold tuning yields consistent performance improvements across various networks and datasets, and that the proposed multiclass score-oriented losses are competitive with standard loss functions, resembling the advantages observed in the binary case.
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