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The Importance of Representation Languages When Extracting Estimation Rules
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
2007 (English)In: Proceedings of the 24th Annual Workshop of the Swedish Artificial Intelligence Society, University College of Borås , 2007, 136-146 p.Conference paper, (Refereed)
Abstract [en]

Data mining is performed to support decision making, but many of the most powerful techniques such as neural networks, or ensembles produce opaque models which are not comprehensible for a human. The lack of interpretability is an obvious disadvantage since decision makers require some sort of explanation before taking action. To achieve comprehensibility, accuracy is often sacrificed by the use of simpler models such as decision trees. Another alternative is, however, to extract rules from the opaque model. We have previously suggested a rule extration algorithm namned G-REX. In this study we further evaluate G-REX on estimation tasks. Two new representation languages are compared to the original, using eight publicly available datasets. The extracted rules are compared to two standard techniques producing comprehensible models; multiple linear regression and the decision tree algorith C&RT. The results show that G-REX outperforms the standard techniques when an appropriate representation is used.

Place, publisher, year, edition, pages
University College of Borås , 2007. 136-146 p.
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-3696OAI: oai:DiVA.org:his-3696DiVA: diva2:293626
Available from: 2010-02-12 Created: 2010-02-12 Last updated: 2013-03-18

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Citation style
  • apa
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