{"ID":2863913,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.25395","arxiv_id":"2509.25395","title":"Crowdsourcing Without People: Modelling Clustering Algorithms as Experts","abstract":"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.","short_abstract":"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 b...","url_abs":"https://arxiv.org/abs/2509.25395","url_pdf":"https://arxiv.org/pdf/2509.25395v1","authors":"[\"Jordyn E. A. Lorentz\",\"Katharine M. Clark\"]","published":"2025-09-29T18:52:37Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"stat.ME\"]","methods":"[]","has_code":false}