{"type":"ScholarlyArticle","title":"Minimum Splits Based Discretization for Continuous Features","authors":[{"name":"Ke Wang","slug":"ke-wang","given":"Ke","family":"Wang","affiliation":""},{"name":"Han Chong Goh","slug":"han-chong-goh","given":"Han Chong","family":"Goh","affiliation":""}],"abstract":"Discretization refers to splitting the range of continuous values into intervals so as to provide useful information about classes. This is usually done by minimizing a goodness measure, subject to constraints such as the maximal number of intervals, the minimal number of examples per interval, or some stopping criterion for splitting. We take a different approach by searching for minimum splits that minimize the number of intervals with respect to a threshold of impurity (i.e., badness). We propose a \"total entropy\" motivated selection of the \"best\" split from minimum splits, without requiring additional constraints. Experiments show that the proposed method produces better decision trees.","doi":null,"url":"https://ijcai.org/proceedings/1997/136","pdf":"https://ijcai.org/proceedings/1997/0136.pdf","markdown":"https://ijcai.org/proceedings/1997/136.md","bibtex":"https://ijcai.org/proceedings/1997/bibtex/136","volume":{"slug":"1997","title":"Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1997","url":"https://ijcai.org/proceedings/1997/","year":1997,"location":"Nagoya, Japan","dates":"","editors":"Martha E. Pollack","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Learning","track":"Learning","number":136,"pages":{"first":942,"last":950},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}