{"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/unsupervised-evaluation-and-weighted","title":"Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions","arxiv_id":"1802.04684","date":"2018-02-13","proceeding":null,"authors":["Mehmet Eren Ahsen","Robert Vogel","Gustavo Stolovitzky"],"abstract":"Learning algorithms that aggregate predictions from an ensemble of diverse\nbase classifiers consistently outperform individual methods. Many of these\nstrategies have been developed in a supervised setting, where the accuracy of\neach base classifier can be empirically measured and this information is\nincorporated in the training process. However, the reliance on labeled data\nprecludes the application of ensemble methods to many real world problems where\nlabeled data has not been curated. To this end we developed a new theoretical\nframework for binary classification, the Strategy for Unsupervised Multiple\nMethod Aggregation (SUMMA), to estimate the performances of base classifiers\nand an optimal strategy for ensemble learning from unlabeled data.","url_abs":"http://arxiv.org/abs/1802.04684v1","url_pdf":"http://arxiv.org/pdf/1802.04684v1.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":"unsupervised-evaluation-and-weighted","repo_url":"https://github.com/learn-ensemble/PY-SUMMA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"unsupervised-evaluation-and-weighted","repo_url":"https://github.com/learn-ensemble/R-SUMMA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}