Crowdsourcing Without People: Modelling Clustering Algorithms as Experts
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912615722123264 |
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| author | Lorentz, Jordyn E. A. Clark, Katharine M. |
| author_facet | Lorentz, Jordyn E. A. Clark, Katharine M. |
| contents | This paper introduces mixsemble, an ensemble method that adapts the Dawid-Skene model to aggregate predictions from multiple model-based clustering algorithms. Unlike traditional crowdsourcing, which relies on human labels, the framework models the outputs of clustering algorithms as noisy annotations. Experiments on both simulated and real-world datasets show that, although the mixsemble is not always the single top performer, it consistently approaches the best result and avoids poor outcomes. This robustness makes it a practical alternative when the true data structure is unknown, especially for non-expert users. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25395 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Crowdsourcing Without People: Modelling Clustering Algorithms as Experts Lorentz, Jordyn E. A. Clark, Katharine M. Machine Learning Methodology This paper introduces mixsemble, an ensemble method that adapts the Dawid-Skene model to aggregate predictions from multiple model-based clustering algorithms. Unlike traditional crowdsourcing, which relies on human labels, the framework models the outputs of clustering algorithms as noisy annotations. Experiments on both simulated and real-world datasets show that, although the mixsemble is not always the single top performer, it consistently approaches the best result and avoids poor outcomes. This robustness makes it a practical alternative when the true data structure is unknown, especially for non-expert users. |
| title | Crowdsourcing Without People: Modelling Clustering Algorithms as Experts |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2509.25395 |