{"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/random-intersection-trees","title":"Random Intersection Trees","arxiv_id":"1303.6223","date":"2013-03-25","proceeding":null,"authors":["Rajen Dinesh Shah","Nicolai Meinshausen"],"abstract":"Finding interactions between variables in large and high-dimensional datasets\nis often a serious computational challenge. Most approaches build up\ninteraction sets incrementally, adding variables in a greedy fashion. The\ndrawback is that potentially informative high-order interactions may be\noverlooked. Here, we propose at an alternative approach for classification\nproblems with binary predictor variables, called Random Intersection Trees. It\nworks by starting with a maximal interaction that includes all variables, and\nthen gradually removing variables if they fail to appear in randomly chosen\nobservations of a class of interest. We show that informative interactions are\nretained with high probability, and the computational complexity of our\nprocedure is of order $p^\\kappa$ for a value of $\\kappa$ that can reach values\nas low as 1 for very sparse data; in many more general settings, it will still\nbeat the exponent $s$ obtained when using a brute force search constrained to\norder $s$ interactions. In addition, by using some new ideas based on min-wise\nhash schemes, we are able to further reduce the computational cost.\nInteractions found by our algorithm can be used for predictive modelling in\nvarious forms, but they are also often of interest in their own right as useful\ncharacterisations of what distinguishes a certain class from others.","url_abs":"http://arxiv.org/abs/1303.6223v1","url_pdf":"http://arxiv.org/pdf/1303.6223v1.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":"random-intersection-trees","repo_url":"https://github.com/sumbose/iRF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}