{"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/active-learning-from-positive-and-unlabeled","title":"Active Learning from Positive and Unlabeled Data","arxiv_id":"1602.07495","date":"2016-02-24","proceeding":null,"authors":["Alireza Ghasemi","Hamid R. Rabiee","Mohsen Fadaee","Mohammad T. Manzuri","Mohammad H. Rohban"],"abstract":"During recent years, active learning has evolved into a popular paradigm for\nutilizing user's feedback to improve accuracy of learning algorithms. Active\nlearning works by selecting the most informative sample among unlabeled data\nand querying the label of that point from user. Many different methods such as\nuncertainty sampling and minimum risk sampling have been utilized to select the\nmost informative sample in active learning. Although many active learning\nalgorithms have been proposed so far, most of them work with binary or\nmulti-class classification problems and therefore can not be applied to\nproblems in which only samples from one class as well as a set of unlabeled\ndata are available.\n  Such problems arise in many real-world situations and are known as the\nproblem of learning from positive and unlabeled data. In this paper we propose\nan active learning algorithm that can work when only samples of one class as\nwell as a set of unlabelled data are available. Our method works by separately\nestimating probability desnity of positive and unlabeled points and then\ncomputing expected value of informativeness to get rid of a hyper-parameter and\nhave a better measure of informativeness./ Experiments and empirical analysis\nshow promising results compared to other similar methods.","url_abs":"http://arxiv.org/abs/1602.07495v1","url_pdf":"http://arxiv.org/pdf/1602.07495v1.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":"active-learning-from-positive-and-unlabeled","repo_url":"https://github.com/aghasemi/alpud","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"informativeness","task_name":"Informativeness"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1602.07495","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}