{"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/the-new-approach-on-fuzzy-decision-trees","title":"The New Approach on Fuzzy Decision Trees","arxiv_id":"1408.3002","date":"2014-08-13","proceeding":null,"authors":["Jooyeol Yun","Jun won Seo","Taeseon Yoon"],"abstract":"Decision trees have been widely used in machine learning. However, due to\nsome reasons, data collecting in real world contains a fuzzy and uncertain\nform. The decision tree should be able to handle such fuzzy data. This paper\npresents a method to construct fuzzy decision tree. It proposes a fuzzy\ndecision tree induction method in iris flower data set, obtaining the entropy\nfrom the distance between an average value and a particular value. It also\npresents an experiment result that shows the accuracy compared to former ID3.","url_abs":"http://arxiv.org/abs/1408.3002v1","url_pdf":"http://arxiv.org/pdf/1408.3002v1.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":"the-new-approach-on-fuzzy-decision-trees","repo_url":"https://github.com/balins/fuzzytree","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"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}