{"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/genesim-genetic-extraction-of-a-single","title":"GENESIM: genetic extraction of a single, interpretable model","arxiv_id":"1611.05722","date":"2016-11-17","proceeding":null,"authors":["Gilles Vandewiele","Olivier Janssens","Femke Ongenae","Filip De Turck","Sofie Van Hoecke"],"abstract":"Models obtained by decision tree induction techniques excel in being\ninterpretable.However, they can be prone to overfitting, which results in a low\npredictive performance. Ensemble techniques are able to achieve a higher\naccuracy. However, this comes at a cost of losing interpretability of the\nresulting model. This makes ensemble techniques impractical in applications\nwhere decision support, instead of decision making, is crucial.\n  To bridge this gap, we present the GENESIM algorithm that transforms an\nensemble of decision trees to a single decision tree with an enhanced\npredictive performance by using a genetic algorithm. We compared GENESIM to\nprevalent decision tree induction and ensemble techniques using twelve publicly\navailable data sets. The results show that GENESIM achieves a better predictive\nperformance on most of these data sets than decision tree induction techniques\nand a predictive performance in the same order of magnitude as the ensemble\ntechniques. Moreover, the resulting model of GENESIM has a very low complexity,\nmaking it very interpretable, in contrast to ensemble techniques.","url_abs":"http://arxiv.org/abs/1611.05722v1","url_pdf":"http://arxiv.org/pdf/1611.05722v1.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":"genesim-genetic-extraction-of-a-single","repo_url":"https://github.com/IBCNServices/GENESIM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"interpretable-machine-learning","task_name":"Interpretable Machine Learning"},{"task_slug":"model","task_name":"model"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}