{"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/unified-framework-for-quantification","title":"Unified Framework for Quantification","arxiv_id":"1606.00868","date":"2016-06-02","proceeding":null,"authors":["Aykut Firat"],"abstract":"Quantification is the machine learning task of estimating test-data class\nproportions that are not necessarily similar to those in training. Apart from\nits intrinsic value as an aggregate statistic, quantification output can also\nbe used to optimize classifier probabilities, thereby increasing classification\naccuracy. We unify major quantification approaches under a constrained\nmulti-variate regression framework, and use mathematical programming to\nestimate class proportions for different loss functions. With this modeling\napproach, we extend existing binary-only quantification approaches to\nmulti-class settings as well. We empirically verify our unified framework by\nexperimenting with several multi-class datasets including the Stanford\nSentiment Treebank and CIFAR-10.","url_abs":"http://arxiv.org/abs/1606.00868v1","url_pdf":"http://arxiv.org/pdf/1606.00868v1.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":"unified-framework-for-quantification","repo_url":"https://github.com/aykutfirat/Quantification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.00868","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}