论文标题
时间概率校准
Temporal Probability Calibration
论文作者
论文摘要
在许多应用中，需要准确的类概率估计值，但是尽管达到可接受的分类准确性，但许多类型的模型仍会产生质量差估计。即使概率校准一直是近期研究的热门话题，但其中大多数已经研究了非序列数据。在本文中，我们考虑校准模型，这些模型从数据序列中产生类概率估计，重点是从不完整序列获得预测的情况下。我们表明，传统的校准技术对于此任务不足以表达，并提出了根据输入序列的长度来调整校准方案的方法。实验评估表明，所提出的方法在校准概率估算的概率估计值通常更为有效，从现代顺序体系结构中，对于一系列应用域的不完整序列。
In many applications, accurate class probability estimates are required, but many types of models produce poor quality probability estimates despite achieving acceptable classification accuracy. Even though probability calibration has been a hot topic of research in recent times, the majority of this has investigated non-sequential data. In this paper, we consider calibrating models that produce class probability estimates from sequences of data, focusing on the case where predictions are obtained from incomplete sequences. We show that traditional calibration techniques are not sufficiently expressive for this task, and propose methods that adapt calibration schemes depending on the length of an input sequence. Experimental evaluation shows that the proposed methods are often substantially more effective at calibrating probability estimates from modern sequential architectures for incomplete sequences across a range of application domains.
