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Relational Learning and Boosting

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Relational Data Mining

Abstract

Boosting, a methodology for constructing and combining multiple classifiers, has been found to lead to substantial improvements in predictive accuracy. Although boosting was formulated in a propositional learning context, the same ideas can be applied to first-order learning (also known as inductive logic programming). Boosting is used here with a system that learns relational definitions of functions. Results show that the magnitude of the improvement, the additional computational cost, and the occasional negative impact of boosting all resemble the corresponding observations for propositional learning.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, Sydney, 2052, Australia

    Ross Quinlan

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  1. Ross Quinlan
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Editor information

Editors and Affiliations

  1. Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia

    Sašo Džeroski  & Nada Lavrač  & 

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© 2001 Springer-Verlag Berlin Heidelberg

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Quinlan, R. (2001). Relational Learning and Boosting. In: Džeroski, S., Lavrač, N. (eds) Relational Data Mining. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04599-2_12

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  • DOI: https://doi.org/10.1007/978-3-662-04599-2_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-07604-6

  • Online ISBN: 978-3-662-04599-2

  • eBook Packages: Springer Book Archive

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