{"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/one-class-splitting-criteria-for-random","title":"One Class Splitting Criteria for Random Forests","arxiv_id":"1611.01971","date":"2016-11-07","proceeding":null,"authors":["Nicolas Goix","Nicolas Drougard","Romain Brault","Maël Chiapino"],"abstract":"Random Forests (RFs) are strong machine learning tools for classification and\nregression. However, they remain supervised algorithms, and no extension of RFs\nto the one-class setting has been proposed, except for techniques based on\nsecond-class sampling. This work fills this gap by proposing a natural\nmethodology to extend standard splitting criteria to the one-class setting,\nstructurally generalizing RFs to one-class classification. An extensive\nbenchmark of seven state-of-the-art anomaly detection algorithms is also\npresented. This empirically demonstrates the relevance of our approach.","url_abs":"http://arxiv.org/abs/1611.01971v3","url_pdf":"http://arxiv.org/pdf/1611.01971v3.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":"one-class-splitting-criteria-for-random","repo_url":"https://github.com/ngoix/ocrf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"one-class-classification","task_name":"One-Class Classification"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}