{"type":"ScholarlyArticle","title":"Flexibly Exploiting Prior Knowledge in Empirical Learning","authors":[{"name":"Julio Ortega","slug":"julio-ortega","given":"Julio","family":"Ortega","affiliation":""},{"name":"Douglas Fisher","slug":"douglas-fisher","given":"Douglas","family":"Fisher","affiliation":""}],"abstract":"This paper presents a method to incorporate knowledge from possibly imperfect models and domain theories into inductive learning of decision trees for classification. The approach assumes that a mode l or domain theory reflects useful prior knowledge of the task. Thus , the default bias should accept the model's predictions as accurate, even in the face of somewhat cont radictory data, which may be unrepresentative or noisy. However , our approach allows the system to a bandon the model or domain theory, or portions thereof, in the face of sufficiently contradictory data . In particular, we use C4 .5 to induce decision t rees from data that hav e been augmented by model or domaint heory-derived 'features ' . We weakly bias the system to select model-derived features during decision tree induction, but this preference is not dogmatically applied. Our experiments vary imperfection in a model, the representativeness of data , and th e veracity with which model-derived f eat ures are preferred .","doi":null,"url":"https://ijcai.org/proceedings/1995/135","pdf":"https://ijcai.org/proceedings/1995/0135.pdf","markdown":"https://ijcai.org/proceedings/1995/135.md","bibtex":"https://ijcai.org/proceedings/1995/bibtex/135","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":135,"pages":{"first":1041,"last":1048},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}