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Why Rule Extraction Matters
Department of Business, University of Borås, Sweden.
Department of Business, University of Borås, Sweden.
University of Skövde, School of Humanities and Informatics.
2004 (English)In: Proceedings of the Eighth IASTED International Conference on Software Engineering and Applications, Cambridge, MA, United States, 9 November 2004 through 11 November 2004 / [ed] M. H. Hamza, 2004, p. 47-52Conference paper, Published paper (Other academic)
Abstract [en]

The purpose of this paper is to argue for rule extraction as an integral part of data mining. The paper contains two case studies where rule extraction is used for typical data mining tasks. More specifically, rule extraction is used both to explain existing opaque models and to produce concept description via an opaque model. The main result is that the rule extraction approach generally yields comprehensible models with higher accuracy than transparent models created directly from the data set by CART and See5. The implication is that rule extraction can enhance the capability of data mining for decision support.

Place, publisher, year, edition, pages
2004. p. 47-52
Keywords [en]
Concept description, CRISP-DM, Data mining, G-REX, Rule extraction
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-1542Scopus ID: 2-s2.0-11144223244ISBN: 0-88986-425-X ISBN: 0889864276 ISBN: 978-088986427-6 OAI: oai:DiVA.org:his-1542DiVA, id: diva2:31818
Conference
Eighth IASTED International Conference on Software Engineering and Applications, Cambridge, MA, United States, 9 November 2004 through 11 November 2004
Available from: 2007-07-09 Created: 2007-07-09 Last updated: 2017-11-27Bibliographically approved

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Niklasson, Lars

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