dc.contributor.author | Everson, Richard M. | |
dc.contributor.author | Fieldsend, Jonathan E. | |
dc.date.accessioned | 2013-07-08T15:12:25Z | |
dc.date.issued | 2006-02-10 | |
dc.description.abstract | Summary
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.citation | In: Multi-Objective Machine Learning, edited by Yaochu Jin, pp. 533-556. Studies in Computational Intelligence, vol 16. | en_GB |
dc.identifier.doi | 10.1007/3-540-33019-4_23 | |
dc.identifier.uri | http://hdl.handle.net/10871/11568 | |
dc.language.iso | en | en_GB |
dc.publisher | Springer | en_GB |
dc.title | Multi-objective optimisation for receiver operating characteristic analysis | en_GB |
dc.type | Book chapter | en_GB |
dc.date.available | 2013-07-08T15:12:25Z | |
dc.contributor.editor | Jin, Y | |
dc.identifier.isbn | 9783540306764 | |
dc.identifier.isbn | 9783540330196 | |
dc.identifier.issn | 1860-949X | |
dc.relation.isPartOf | Multi-objective machine learning | |
dc.description | Copyright © 2006 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.com | en_GB |
dc.identifier.journal | Studies in Computational Intelligence | en_GB |