{"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/a-projection-pursuit-forest-algorithm-for","title":"A Projection Pursuit Forest Algorithm for Supervised Classification","arxiv_id":"1807.07207","date":"2018-07-19","proceeding":null,"authors":["Natalia da Silva","Dianne Cook","Eun-Kyung Lee"],"abstract":"This paper presents a new ensemble learning method for classification\nproblems called projection pursuit random forest (PPF). PPF uses the PPtree\nalgorithm introduced in Lee et al. (2013). In PPF, trees are constructed by\nsplitting on linear combinations of randomly chosen variables. Projection\npursuit is used to choose a projection of the variables that best separates the\nclasses. Utilizing linear combinations of variables to separate classes takes\nthe correlation between variables into account which allows PPF to outperform a\ntraditional random forest when separations between groups occurs in\ncombinations of variables.\n  The method presented here can be used in multi-class problems and is\nimplemented into an R (R Core Team, 2018) package, PPforest, which is available\non CRAN, with development versions at https://github.com/natydasilva/PPforest.","url_abs":"http://arxiv.org/abs/1807.07207v2","url_pdf":"http://arxiv.org/pdf/1807.07207v2.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":"a-projection-pursuit-forest-algorithm-for","repo_url":"https://github.com/natydasilva/PPforest","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"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}