{"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/binary-classification-from-positive","title":"Binary Classification from Positive-Confidence Data","arxiv_id":"1710.07138","date":"2017-10-19","proceeding":"NeurIPS 2018 12","authors":["Takashi Ishida","Gang Niu","Masashi Sugiyama"],"abstract":"Can we learn a binary classifier from only positive data, without any\nnegative data or unlabeled data? We show that if one can equip positive data\nwith confidence (positive-confidence), one can successfully learn a binary\nclassifier, which we name positive-confidence (Pconf) classification. Our work\nis related to one-class classification which is aimed at \"describing\" the\npositive class by clustering-related methods, but one-class classification does\nnot have the ability to tune hyper-parameters and their aim is not on\n\"discriminating\" positive and negative classes. For the Pconf classification\nproblem, we provide a simple empirical risk minimization framework that is\nmodel-independent and optimization-independent. We theoretically establish the\nconsistency and an estimation error bound, and demonstrate the usefulness of\nthe proposed method for training deep neural networks through experiments.","url_abs":"http://arxiv.org/abs/1710.07138v3","url_pdf":"http://arxiv.org/pdf/1710.07138v3.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":"binary-classification-from-positive","repo_url":"https://github.com/takashiishida/pconf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"one-class-classification","task_name":"One-Class Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.07138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.07138"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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