{"type":"ScholarlyArticle","title":"Constructive Induction On Decision Trees","authors":[{"name":"Christopher J. Matheus","slug":"christopher-j-matheus","given":"Christopher J.","family":"Matheus","affiliation":""},{"name":"Larry A. Rendell","slug":"larry-a-rendell","given":"Larry A.","family":"Rendell","affiliation":""}],"abstract":"Selective induction techniques perform poorly when the features are inappropriate for the target concept. One solution is to have the learning system construct new features automatically; unfortunately feature construction is a difficult and poorly understood problem . In this paper we present a definition of feature construction in concept learning, and offer a framework for its study based on four aspects: detection, selection, generalization, and evaluation. This framework is used in the analysis of existing learning systems and as the basis for the design of a new system, CITRE. CITRE performs feature construction using decision trees and simple domain knowledge as constructive biases. Initial results on a set of spatial-dependent problems suggest the importance of domain knowledge and feature generalization, i.e., constructive induction.","doi":null,"url":"https://ijcai.org/proceedings/1989/103","pdf":"https://ijcai.org/proceedings/1989/0103.pdf","markdown":"https://ijcai.org/proceedings/1989/103.md","bibtex":"https://ijcai.org/proceedings/1989/bibtex/103","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":103,"pages":{"first":645,"last":649},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}