{"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/approximate-false-positive-rate-control-in","title":"Approximate False Positive Rate Control in Selection Frequency for Random Forest","arxiv_id":"1410.2838","date":"2014-10-10","proceeding":null,"authors":["Ender Konukoglu","Melanie Ganz"],"abstract":"Random Forest has become one of the most popular tools for feature selection.\nIts ability to deal with high-dimensional data makes this algorithm especially\nuseful for studies in neuroimaging and bioinformatics. Despite its popularity\nand wide use, feature selection in Random Forest still lacks a crucial\ningredient: false positive rate control. To date there is no efficient,\nprincipled and computationally light-weight solution to this shortcoming. As a\nresult, researchers using Random Forest for feature selection have to resort to\nusing heuristically set thresholds on feature rankings. This article builds an\napproximate probabilistic model for the feature selection process in random\nforest training, which allows us to compute an estimated false positive rate\nfor a given threshold on selection frequency. Hence, it presents a principled\nway to determine thresholds for the selection of relevant features without any\nadditional computational load. Experimental analysis with synthetic data\ndemonstrates that the proposed approach can limit false positive rates on the\norder of the desired values and keep false negative rates low. Results show\nthat this holds even in the presence of a complex correlation structure between\nfeatures. Its good statistical properties and light-weight computational needs\nmake this approach widely applicable to feature selection for a wide-range of\napplications.","url_abs":"http://arxiv.org/abs/1410.2838v1","url_pdf":"http://arxiv.org/pdf/1410.2838v1.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":"approximate-false-positive-rate-control-in","repo_url":"https://github.com/aberHRML/forestControl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}