Measuring Performance when Positives are Rare : Relative Advantage versus Predictive

This paper presents a new method of measuring performance when positives are rare and investigates whether Chomsky-like grammar representations are useful for learning accurate comprehensible predic-tors of members of biological sequence families. The positive-only learning framework of the Inductive Logic Programming (ILP) system CPro-gol is used to generate a grammar for recognising a class of proteins known as human neuropeptide precursors (NPPs). 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 lter is more than 100 times more ee-cient than randomly selecting proteins for synthesis and testing them for biological activity. Predictive accuracy is not a good measure of performance for this domain because it does not discriminate well between NPP recognition models: despite covering varying numbers of (the rare) positives, all the models are awarded a similar (high) score by predictive accuracy because they all exclude most of the abundant negatives.