L-Modified ILP Evaluation Functions for Positive-Only Biological Grammar Learning

Authors:
Thierry Mamer;Christopher H. Bryant;John Mccall
Affiliations:
School of Computing, The Robert Gordon University, Aberdeen, Scotland, UK AB25 1HG;School of Computing, Science and Engineering, University of Salford, Salford, UK M5 4WT;School of Computing, The Robert Gordon University, Aberdeen, Scotland, UK AB25 1HG
Venue:
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Year:
2008

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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.