Show simple item record  

dc.contributor.authorBranco, Paulaen_NZ
dc.contributor.authorTorgo, Luísen_NZ
dc.contributor.authorRibeiro, Rita P.en_NZ
dc.contributor.authorFrank, Eibeen_NZ
dc.contributor.authorPfahringer, Bernharden_NZ
dc.contributor.authorRau, Markus Michaelen_NZ
dc.coverage.spatialTokyo, JAPANen_NZ
dc.date.accessioned2019-05-07T23:07:34Z
dc.date.available2017-01-01en_NZ
dc.date.available2019-05-07T23:07:34Z
dc.date.issued2017en_NZ
dc.identifier.citationBranco, P., Torgo, L., Ribeiro, R. P., Frank, E., Pfahringer, B., & Rau, M. M. (2017). Learning Through Utility Optimization in Regression Tasks. In Proceeding of 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 30–39). Washington, DC, USA: IEEE. https://doi.org/10.1109/DSAA.2017.63en
dc.identifier.issn2472-1573en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12515
dc.description.abstractAccounting for misclassification costs is important in many practical applications of machine learning, and cost-sensitive techniques for classification have been studied extensively. Utility-based learning provides a generalization of purely cost-based approaches that considers both costs and benefits, enabling application to domains with complex cost-benefit settings. However, there is little work on utility- or cost-based learning for regression. In this paper, we formally define the problem of utility-based regression and propose a strategy for maximizing the utility of regression models. We verify our findings in a large set of experiments that show the advantage of our proposal in a diverse set of domains, learning algorithms and cost/benefit settings.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEEen_NZ
dc.rights© 2017 IEEE. This is the author's version of the work. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.source4th IEEE / ACM / ASA International Conference on Data Science and Advanced Analytics (DSAA)en_NZ
dc.subjectScience & Technologyen_NZ
dc.subjectTechnologyen_NZ
dc.subjectComputer Science, Information Systemsen_NZ
dc.subjectComputer Science, Theory & Methodsen_NZ
dc.subjectEngineering, Electrical & Electronicen_NZ
dc.subjectComputer Scienceen_NZ
dc.subjectEngineeringen_NZ
dc.subjectDENSITY-ESTIMATIONen_NZ
dc.subjectMachine learning
dc.titleLearning Through Utility Optimization in Regression Tasksen_NZ
dc.typeConference Contribution
dc.identifier.doi10.1109/DSAA.2017.63en_NZ
dc.relation.isPartOfProceeding of 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA)en_NZ
pubs.begin-page30
pubs.elements-id224205
pubs.end-page39
pubs.finish-date2017-10-21en_NZ
pubs.place-of-publicationWashington, DC, USA
pubs.publication-statusPublisheden_NZ
pubs.start-date2017-10-19en_NZ


Files in this item

This item appears in the following Collection(s)

Show simple item record