{"type":"ScholarlyArticle","title":"Learning DNF by Decision Trees","authors":[{"name":"Giulia Pagallo","slug":"giulia-pagallo","given":"Giulia","family":"Pagallo","affiliation":""}],"abstract":"We investigate the problem of learning DNF concepts from examples using decision trees as a concept description language. Due to the replication problem, DNF concepts do not always have a concise decision tree description when the tests at the nodes are limited to the initial attributes . However, the representational complexity may be overcome by using high level attributes as tests. We present a novel algorithm that modifies the initial bias determined by the primitive attributes by adaptively enlarging the attribute set with high level attributes. We show empirically that this algorithm outperforms a standard decision tree algorithm for learning small random DNF with and without noise, when the examples are drawn from the uniform distribution.","doi":null,"url":"https://ijcai.org/proceedings/1989/102","pdf":"https://ijcai.org/proceedings/1989/0102.pdf","markdown":"https://ijcai.org/proceedings/1989/102.md","bibtex":"https://ijcai.org/proceedings/1989/bibtex/102","volume":{"slug":"1989","title":"Proceedings of the Eleventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1989","url":"https://ijcai.org/proceedings/1989/","year":1989,"location":"Detroit, Michigan, USA","dates":"August 20-25 1989","editors":"N. S. Sridharan","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Machine Learning","track":"Machine Learning","number":102,"pages":{"first":639,"last":643},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}