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

Mamer, T, Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343 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, 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:	Schools > School of Computing, Science and Engineering Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Publisher:	Springer
Refereed:	Yes
Series Name:	Lecture notes in artificial intelligence
ISBN:	9783540859277
Related URLs:	Author
Depositing User:	Dr Chris H. Bryant
Date Deposited:	16 Feb 2009 13:41
Last Modified:	19 Feb 2019 14:07
URI:	http://usir.salford.ac.uk/id/eprint/1753

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