{"type":"ScholarlyArticle","title":"Oversearching and Layered Search in Empirical Learning","authors":[{"name":"J. Ross Quinlan","slug":"j-ross-quinlan","given":"J. Ross","family":"Quinlan","affiliation":""},{"name":"R. Mike Cameron-Jones","slug":"r-mike-cameron-jones","given":"R. Mike","family":"Cameron-Jones","affiliation":""}],"abstract":"When learning classifiers, more extensive search for rules is shown to lead to lower predictive accuracy on many of the real-world domains investigated. This counter-intuitive result is particularly relevant to recent systematic search methods that use risk-free pruning to achieve the same outcome as exhaustive search. We propose an iterated search method that commences with greedy search, extending its scope at each iteration until a stopping criterion is satisfied. This layered search is often found to produce theories that are more accurate than those obtained with either greedy search or moderately extensive beam search.","doi":null,"url":"https://ijcai.org/proceedings/1995/132","pdf":"https://ijcai.org/proceedings/1995/0132.pdf","markdown":"https://ijcai.org/proceedings/1995/132.md","bibtex":"https://ijcai.org/proceedings/1995/bibtex/132","volume":{"slug":"1995","title":"Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1995","url":"https://ijcai.org/proceedings/1995/","year":1995,"location":"Montréal, Québec, Canada","dates":"","editors":"Chris S. Mellish","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Learning","track":"Learning","number":132,"pages":{"first":1019,"last":1023},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}