Learning under Temporal Label Noise
Fuente:
arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909537937653760 |
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| author | Nagaraj, Sujay Gerych, Walter Tonekaboni, Sana Goldenberg, Anna Ustun, Berk Hartvigsen, Thomas |
| author_facet | Nagaraj, Sujay Gerych, Walter Tonekaboni, Sana Goldenberg, Anna Ustun, Berk Hartvigsen, Thomas |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04398 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning under Temporal Label Noise Nagaraj, Sujay Gerych, Walter Tonekaboni, Sana Goldenberg, Anna Ustun, Berk Hartvigsen, Thomas Machine Learning Artificial Intelligence 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 |
| title | Learning under Temporal Label Noise |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2402.04398 |