{"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/optimal-binary-classifier-aggregation-for","title":"Optimal Binary Classifier Aggregation for General Losses","arxiv_id":"1510.00452","date":"2015-10-01","proceeding":"NeurIPS 2016 12","authors":["Akshay Balsubramani","Yoav Freund"],"abstract":"We address the problem of aggregating an ensemble of predictors with known\nloss bounds in a semi-supervised binary classification setting, to minimize\nprediction loss incurred on the unlabeled data. We find the minimax optimal\npredictions for a very general class of loss functions including all convex and\nmany non-convex losses, extending a recent analysis of the problem for\nmisclassification error. The result is a family of semi-supervised ensemble\naggregation algorithms which are as efficient as linear learning by convex\noptimization, but are minimax optimal without any relaxations. Their decision\nrules take a form familiar in decision theory -- applying sigmoid functions to\na notion of ensemble margin -- without the assumptions typically made in\nmargin-based learning.","url_abs":"http://arxiv.org/abs/1510.00452v5","url_pdf":"http://arxiv.org/pdf/1510.00452v5.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":"optimal-binary-classifier-aggregation-for","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":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"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}