ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866909060343791616 |
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| author | Sedova, Anastasiia Roth, Benjamin |
| author_facet | Sedova, Anastasiia Roth, Benjamin |
| contents | A cost-effective alternative to manual data labeling is weak supervision (WS), where data samples are automatically annotated using a predefined set of labeling functions (LFs), rule-based mechanisms that generate artificial labels for the associated classes. In this work, we investigate noise reduction techniques for WS based on the principle of k-fold cross-validation. We introduce a new algorithm ULF for Unsupervised Labeling Function correction, which denoises WS data by leveraging models trained on all but some LFs to identify and correct biases specific to the held-out LFs. Specifically, ULF refines the allocation of LFs to classes by re-estimating this assignment on highly reliable cross-validated samples. Evaluation on multiple datasets confirms ULF's effectiveness in enhancing WS learning without the need for manual labeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_06863 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision Sedova, Anastasiia Roth, Benjamin Machine Learning A cost-effective alternative to manual data labeling is weak supervision (WS), where data samples are automatically annotated using a predefined set of labeling functions (LFs), rule-based mechanisms that generate artificial labels for the associated classes. In this work, we investigate noise reduction techniques for WS based on the principle of k-fold cross-validation. We introduce a new algorithm ULF for Unsupervised Labeling Function correction, which denoises WS data by leveraging models trained on all but some LFs to identify and correct biases specific to the held-out LFs. Specifically, ULF refines the allocation of LFs to classes by re-estimating this assignment on highly reliable cross-validated samples. Evaluation on multiple datasets confirms ULF's effectiveness in enhancing WS learning without the need for manual labeling. |
| title | ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2204.06863 |