{"type":"ScholarlyArticle","title":"Induction of Oblique Decision Trees","authors":[{"name":"David G. Heath","slug":"david-g-heath","given":"David G.","family":"Heath","affiliation":""},{"name":"Simon Kasif","slug":"simon-kasif","given":"Simon","family":"Kasif","affiliation":""},{"name":"Steven Salzberg","slug":"steven-salzberg","given":"Steven","family":"Salzberg","affiliation":""}],"abstract":"This paper introduces a randomized technique for partitioning examples using oblique hyperplanes. Standard decision tree techniques, such as ID3 and its descendants, partition a set of points with axis-parallel hyperplanes. Our method, by contrast, attempts to find hyperplanes at any orientation. The purpose of this more general technique is to find smaller but equally accurate decision trees than those created by other methods. We have tested our algorithm on both real and simulated data, and found that in some cases it produces surprisingly small trees without losing predictive accuracy. Small trees allow us, in turn, to obtain simple qualitative descriptions of each problem domain.","doi":null,"url":"https://ijcai.org/proceedings/1993/138","pdf":"https://ijcai.org/proceedings/1993/0138.pdf","markdown":"https://ijcai.org/proceedings/1993/138.md","bibtex":"https://ijcai.org/proceedings/1993/bibtex/138","volume":{"slug":"1993","title":"Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1993","url":"https://ijcai.org/proceedings/1993/","year":1993,"location":"Chambéry, France","dates":"","editors":"Ruzena Bajcsy","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Machine Learning","track":"Machine Learning","number":138,"pages":{"first":1002,"last":1006},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}