Measuring performance when positives are rare: relative advantage versus predictive accuracy - a biological case-study

Muggleton, SH, Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343 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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Abstract

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.

Item Type:	Book Section
Editors:	de Mántaras, RL and Plaza, E
Additional Information:	Paper originally presented at the 11th European Conference on Machine Learning Barcelona, Catalonia, Spain, May 31 – June 2, 2000 Proceedings.
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 computer science
ISBN:	9783540676027
Related URLs:	Author
Depositing User:	Dr Chris H. Bryant
Date Deposited:	17 Feb 2009 12:16
Last Modified:	16 Feb 2022 08:16
URI:	https://usir.salford.ac.uk/id/eprint/1764

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