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Accuracy vs. comprehensibility in data mining models
University of Skövde, School of Humanities and Informatics.
2004 (English)In: Proceedings of the Seventh International Conference on Information Fusion: 28 June - 1 July 2004 Stockholm Sweden, 2004, 295-300 p.Conference paper, (Other academic)
Abstract [en]

This paper addresses the important issue of the tradeoff between accuracy and comprehensibility in data mining. The paper presents results which show that it is, to some extent, possible to bridge this gap. A method for rule extraction from opaque models (Genetic Rule EXtraction – G-REX) is used to show the effects on accuracy when forcing the creation of comprehensible representations. In addition the technique of combining different classifiers to an ensemble is demonstrated on some well-known data sets. The results show that ensembles generally have very high accuracy, thus making them a good first choice when performing predictive data mining.

Place, publisher, year, edition, pages
2004. 295-300 p.
Keyword [en]
data mining, ensembles, rule extraction
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-1500Scopus ID: 2-s2.0-6344291509OAI: oai:DiVA.org:his-1500DiVA: diva2:31776
Conference
The 7th International Conference on Information Fusion, June 28 to July 1, 2004 in Stockholm, Sweden
Available from: 2007-10-10 Created: 2007-10-10 Last updated: 2014-08-21Bibliographically approved

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Niklasson, LarsKönig, Rikard
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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