Show simple item record

dc.contributor.authorEverson, Richard M.
dc.contributor.authorFieldsend, Jonathan E.
dc.date.accessioned2013-07-08T15:12:25Z
dc.date.issued2006
dc.description.abstractSummary Receiver operating characteristic (ROC) analysis is now a standard tool for the comparison of binary classifiers and the selection operating parameters when the costs of misclassification are unknown. This chapter outlines the use of evolutionary multi-objective optimisation techniques for ROC analysis, in both its traditional binary classification setting, and in the novel multi-class ROC situation. Methods for comparing classifier performance in the multi-class case, based on an analogue of the Gini coefficient, are described, which leads to a natural method of selecting the classifier operating point. Illustrations are given concerning synthetic data and an application to Short Term Conflict Alert.en_GB
dc.identifier.citationVol. 16, pp. 533-556en_GB
dc.identifier.doi10.1007/3-540-33019-4_23
dc.identifier.urihttp://hdl.handle.net/10871/11568
dc.language.isoenen_GB
dc.publisherSpringer Berlin Heidelbergen_GB
dc.relation.urlhttp://dx.doi.org/10.1007/3-540-33019-4_23en_GB
dc.titleMulti-objective optimisation for receiver operating characteristic analysisen_GB
dc.typeArticleen_GB
dc.typeBook chapteren_GB
dc.date.available2013-07-08T15:12:25Z
dc.contributor.editorJin, Y
dc.identifier.isbn9783540306764
dc.identifier.isbn9783540330196
dc.identifier.issn1860-949X
dc.relation.isPartOfMulti-objective machine learning
dc.descriptionCopyright © 2006 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comen_GB
dc.descriptionBook title: Multi-Objective Machine Learningen_GB
dc.identifier.journalStudies in Computational Intelligenceen_GB


Files in this item

This item appears in the following Collection(s)

Show simple item record