Measuring Performance when Positives are Rare
Stephen Muggleton, Christopher H. Bryant, Ashwin Srinivasan · 2000
This paper presents a new method of measuring performance when positives are rare and investigates whether Chomsky-like grammar representations are useful for learning accu-rate comprehensible predictors of members of biological se-quence families. The positive-only learning framework of the Inductive Logic Programming (ILP) system CProgol is used to generate a grammar for recognising a class of pro-teins known as human neuropeptide precursors (NPPs). As far as these authors are aware, this is both the first biological grammar learnt using ILP and the first real-world scientific application of the positive-only learning framework of CPro-gol. Performance is measured using both predictive accuracy and a new cost function, Relative Advantage (RA). The RA results show that searching for NPPs by using our best NPP predictor as a filter is more than 100 times more efficient than randomly selecting proteins for synthesis and testing them for biological activity. The highest RA was achieved by a model which includes grammar-derived features. ThisRA is signif-icantly higher than the best RA achieved without the use of the grammar-derived features.