Learning under Temporal Label Noise
This addresses a previously unstudied issue for sequential classification tasks, such as in healthcare or finance, where label noise changes over time, though it is incremental in improving existing noise-handling techniques.
The paper tackles the problem of temporal label noise in time series classification, where label quality varies over time, and proposes methods to train noise-tolerant classifiers by estimating the temporal noise function, achieving state-of-the-art performance on real-world datasets.
Many time series classification tasks, where labels vary over time, are affected by label noise that also 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 over time while being corrupted by a time-dependent noise function. We first demonstrate the importance of modeling the temporal nature of the label noise function and how existing methods will consistently underperform. We then propose methods to 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 under diverse types of temporal label noise on real-world datasets