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L-modified ILP evaluation functions for positive-only biological grammar learning

Mamer, T, Bryant, CH and McCall, JM 2008, 'L-modified ILP evaluation functions for positive-only biological grammar learning' , in: Inductive logic programming , Lecture notes in artificial intelligence (5194) , Springer-Verlag, Berlin / Heidelberg, Germany, pp. 176-191.

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    Abstract

    We identify a shortcoming of a standard positive-only clause evaluation function within the context of learning biological grammars. To overcome this shortcoming we propose L-modification, a modification to this evaluation function such that the lengths of individual examples are considered. We use a set of bio-sequences known as neuropeptide precursor middles (NPP-middles). Using L-modification to learn from these NPP-middles results in induced grammars that have a better performance than that achieved when using the standard positive-only clause evaluation function. We also show that L-modification improves the performance of induced grammars when learning on short, medium or long NPPs-middles. A potential disadvantage of L-modification is discussed. Finally, we show that, as the limit on the search space size increases, the greater is the increase in predictive performance arising from L-modification.

    Item Type: Book Section
    Editors: Zelezny, F and Lavrac, N
    Additional Information: Paper originally presented at the 18th International Conference, ILP 2008 Prague, Czech Republic, September 10-12 2008
    Uncontrolled Keywords: inductive logic programming (ILP), biological grammar induction, machine learning, minimum description length (MDL)
    Themes: Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science
    Subjects / Themes > Q Science > QH Natural history > QH301 Biology
    Subjects outside of the University Themes
    Schools: Colleges and Schools > College of Science & Technology
    Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering
    Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
    Publisher: Springer-Verlag
    Refereed: Yes
    ISBN: 9783540859277
    Related URLs:
    Depositing User: Dr Chris H. Bryant
    Date Deposited: 16 Feb 2009 13:41
    Last Modified: 20 Aug 2013 16:55
    URI: http://usir.salford.ac.uk/id/eprint/1753

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