Many sequential classification tasks are affected by label noise that varies over time. Such noise can cause label quality to improve, worsen, or periodically change over time. We first propose and formalize temporal label noise, an unstudied problem for sequential classification of time series. In this setting, multiple labels are recorded in sequence while being corrupted by a time-dependent noise function. We first demonstrate the importance of modelling the temporal nature of the label noise function and how existing methods will consistently underperform. We then propose methods that can train noise-tolerant classifiers by estimating the temporal label noise function directly from data. We show that our methods lead to state-of-the-art performance in the presence of diverse temporal label noise functions using real and synthetic data.

翻译：许多序列分类任务受到随时间变化的标签噪声影响。此类噪声可能导致标签质量随时间改善、恶化或周期性变化。本文首先提出并形式化了时态标签噪声这一未被研究的时间序列序列分类问题。在该设定中，多个标签按顺序记录的同时受到时间相关噪声函数的干扰。我们首先论证了建模标签噪声函数时态特性的重要性，以及现有方法将持续表现不足的原因。随后提出了通过直接从数据中估计时态标签噪声函数来训练抗噪分类器的方法。实验结果表明，在真实与合成数据上面对多样化的时态标签噪声函数时，我们的方法达到了最先进的性能。