{"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/scalable-semi-supervised-aggregation-of","title":"Scalable Semi-Supervised Aggregation of Classifiers","arxiv_id":"1506.05790","date":"2015-06-18","proceeding":"NeurIPS 2015 12","authors":["Akshay Balsubramani","Yoav Freund"],"abstract":"We present and empirically evaluate an efficient algorithm that learns to\naggregate the predictions of an ensemble of binary classifiers. The algorithm\nuses the structure of the ensemble predictions on unlabeled data to yield\nsignificant performance improvements. It does this without making assumptions\non the structure or origin of the ensemble, without parameters, and as scalably\nas linear learning. We empirically demonstrate these performance gains with\nrandom forests.","url_abs":"http://arxiv.org/abs/1506.05790v2","url_pdf":"http://arxiv.org/pdf/1506.05790v2.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":"scalable-semi-supervised-aggregation-of","repo_url":"https://github.com/aikanor/marvin","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}