Measuring performance when positives are rare: relative advantage versus predictive accuracy - a biological case-study
Muggleton, SH, Bryant, CH and Srinivasan, A 2000, 'Measuring performance when positives are rare: relative advantage versus predictive accuracy - a biological case-study' , in: Machine learning: ECML 2000: 11th European conference on machine learning, Barcelona, Catalonia, Spain, May 31-June 2 2000 , Lecture notes in computer science (1810) , Springer, Berlin / Heidelberg, Germany, pp. 300-312.
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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 predictors of members of biological sequence 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 proteins known as human neuropeptide precursors (NPPs). Performance is measured using both predictive accuracy and a new cost function, em 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. 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.
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