{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/analysis-of-minimax-error-rate-for","title":"Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model","arxiv_id":"1802.04551","date":"2018-02-13","proceeding":"ICML 2018 7","authors":["Hideaki Imamura","Issei Sato","Masashi Sugiyama"],"abstract":"While crowdsourcing has become an important means to label data, there is\ngreat interest in estimating the ground truth from unreliable labels produced\nby crowdworkers. The Dawid and Skene (DS) model is one of the most well-known\nmodels in the study of crowdsourcing. Despite its practical popularity,\ntheoretical error analysis for the DS model has been conducted only under\nrestrictive assumptions on class priors, confusion matrices, or the number of\nlabels each worker provides. In this paper, we derive a minimax error rate\nunder more practical setting for a broader class of crowdsourcing models\nincluding the DS model as a special case. We further propose the worker\nclustering model, which is more practical than the DS model under real\ncrowdsourcing settings. The wide applicability of our theoretical analysis\nallows us to immediately investigate the behavior of this proposed model, which\ncan not be analyzed by existing studies. Experimental results showed that there\nis a strong similarity between the lower bound of the minimax error rate\nderived by our theoretical analysis and the empirical error of the estimated\nvalue.","url_abs":"http://arxiv.org/abs/1802.04551v2","url_pdf":"http://arxiv.org/pdf/1802.04551v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"analysis-of-minimax-error-rate-for","repo_url":"https://github.com/HideakiImamura/MinimaxErrorRate","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1802.04551","atlas_url":"https://app.syntology.ai/?focus=1802.04551","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}