{"type":"ScholarlyArticle","title":"Stacked Generalizations: When Does It Work?","authors":[{"name":"Kai Ming Ting","slug":"kai-ming-ting","given":"Kai Ming","family":"Ting","affiliation":""},{"name":"Ian H. Witten","slug":"ian-h-witten","given":"Ian H.","family":"Witten","affiliation":""}],"abstract":"Stacked generalization is a general method of using a high-level model to combine lowerlevel models to achieve greater predictive accuracy. In this paper we address two crucial issues which have been considered to be a 'black art' in classification tasks ever since the introduction of stacked generalization in 1992 by Wolpert: the type of generalizer that is suitable to derive the higher-level model, and the kind of attributes that should be used as its input. We demonstrate the effectiveness of stacked generalization for combining three different types of learning algorithms.","doi":null,"url":"https://ijcai.org/proceedings/1997/125","pdf":"https://ijcai.org/proceedings/1997/0125.pdf","markdown":"https://ijcai.org/proceedings/1997/125.md","bibtex":"https://ijcai.org/proceedings/1997/bibtex/125","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":125,"pages":{"first":866,"last":872},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}