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dc.contributor.authorBertsimas, Dimitris
dc.contributor.authorDunn, Jack
dc.contributor.authorGibson, Emma
dc.contributor.authorOrfanoudaki, Agni
dc.date.accessioned2022-04-04T19:46:28Z
dc.date.available2022-04-04T13:01:48Z
dc.date.available2022-04-04T19:46:28Z
dc.date.issued2022-04
dc.date.submitted2021-10
dc.identifier.issn0885-6125
dc.identifier.issn1573-0565
dc.identifier.urihttps://hdl.handle.net/1721.1/141634.2
dc.description.abstractAbstract Tree-based models are increasingly popular due to their ability to identify complex relationships that are beyond the scope of parametric models. Survival tree methods adapt these models to allow for the analysis of censored outcomes, which often appear in medical data. We present a new Optimal Survival Trees algorithm that leverages mixed-integer optimization (MIO) and local search techniques to generate globally optimized survival tree models. We demonstrate that the OST algorithm improves on the accuracy of existing survival tree methods, particularly in large datasets.en_US
dc.publisherSpringer Science and Business Media LLCen_US
dc.relation.isversionofhttps://doi.org/10.1007/s10994-021-06117-0en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceSpringer USen_US
dc.titleOptimal survival treesen_US
dc.typeArticleen_US
dc.identifier.citationBertsimas, Dimitris, Dunn, Jack, Gibson, Emma and Orfanoudaki, Agni. 2022. "Optimal survival trees."en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Center
dc.relation.journalMachine Learningen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-04-03T03:13:10Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.embargo.termsN
dspace.date.submission2022-04-03T03:13:10Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work Neededen_US


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