dc.contributor.author | Petrides, George | |
dc.contributor.author | Verbeke, Wouter | |
dc.date.accessioned | 2022-04-04T08:07:05Z | |
dc.date.available | 2022-04-04T08:07:05Z | |
dc.date.created | 2021-10-06T22:46:21Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 1384-5810 | |
dc.identifier.uri | https://hdl.handle.net/11250/2989430 | |
dc.description.abstract | Over the years, a plethora of cost-sensitive methods have been proposed for learning on data when different types of misclassification errors incur different costs. Our contribution is a unifying framework that provides a comprehensive and insightful overview on cost-sensitive ensemble methods, pinpointing their differences and similarities via a fine-grained categorization. Our framework contains natural extensions and generalisations of ideas across methods, be it AdaBoost, Bagging or Random Forest, and as a result not only yields all methods known to date but also some not previously considered. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Cost-sensitive ensemble learning: a unifying framework | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | Copyright 2021 The Author(s) | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |
dc.identifier.doi | 10.1007/s10618-021-00790-4 | |
dc.identifier.cristin | 1943965 | |
dc.source.journal | Data mining and knowledge discovery | en_US |
dc.source.pagenumber | 1-28 | en_US |
dc.identifier.citation | Data mining and knowledge discovery. 2022, 36, 1-28. | en_US |
dc.source.volume | 36 | en_US |