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dc.contributor.authorFieldsend, Jonathan E.
dc.contributor.authorEverson, Richard M.
dc.date.accessioned2013-07-10T12:51:33Z
dc.date.issued2008
dc.description.abstractWhen optimising receiver operating characteristic (ROC) curves there is an inherent degree of uncertainty associated with the operating point evaluation of a model parameterisation x. This is due to the finite amount of training data used to evaluate the true and false positive rates of x. The uncertainty associated with any particular x can be reduced, but only at the computation cost of evaluating more data. Here we explicitly represent this uncertainty through the use of probabilistically non-dominated archives, and show how expensive ROC optimisation problems may be tackled by only evaluating a small subset of the available data at each generation of an optimisation algorithm. Illustrative results are given on data sets from the well known UCI machine learning repository.en_GB
dc.identifier.citationProceedings of the IEEE Congress on Evolutionary Computation 2008 (CEC 2008). (IEEE World Congress on Computational Intelligence), pp. 3984 - 3991en_GB
dc.identifier.doi10.1109/CEC.2008.4631340
dc.identifier.urihttp://hdl.handle.net/10871/11684
dc.language.isoenen_GB
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_GB
dc.relation.urlhttp://dx.doi.org/10.1109/CEC.2008.4631340en_GB
dc.subjectoptimisationen_GB
dc.subjectpattern classificationen_GB
dc.subjectsensitivity analysisen_GB
dc.subjectuncertain systemsen_GB
dc.subjectAlgorithm design and analysisen_GB
dc.subjectComputational efficiencyen_GB
dc.subjectControl systemsen_GB
dc.subjectCost functionen_GB
dc.subjectDisplaysen_GB
dc.subjectMachine learningen_GB
dc.subjectOptimization methodsen_GB
dc.subjectPerformance evaluationen_GB
dc.subjectSignal processingen_GB
dc.subjectUncertaintyen_GB
dc.titleOn the efficient use of uncertainty when performing expensive ROC optimisation.en_GB
dc.typeArticleen_GB
dc.typeConference paperen_GB
dc.date.available2013-07-10T12:51:33Z
dc.identifier.isbn9781424418237
dc.identifier.isbn9781424418220
dc.descriptionCopyright © 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en_GB
dc.descriptionIEEE Congress on Evolutionary Computation 2008 (CEC 2008). (IEEE World Congress on Computational Intelligence), Hong Kong, 1-6 June 2008en_GB


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