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dc.contributor.supervisorJafari-Jozani, Mohammad
dc.contributor.authorUzelmann, Keith
dc.date.accessioned2022-08-24T20:03:10Z
dc.date.available2022-08-24T20:03:10Z
dc.date.copyright2022-08-24
dc.date.issued2022-08-24
dc.date.submitted2022-08-24T19:53:42Zen_US
dc.identifier.urihttp://hdl.handle.net/1993/36752
dc.description.abstractThis thesis concerns a novel approach to addressing the issue of class imbalance in machine learning, in particular by modifying the sampling technique itself. Through the use of convex optimization, statistical learning theory, and numerical simulation, the technique of Nomination Sampling is applied to the Logistic regression and Support Vector Machine classifiers to obtain a better training set. Further, this sampling technique is based off of expert opinion, which until now has received only minimal attention in the machine learning community. Efficient algorithms for solving the proposed learning problems are given, and numerical studies are performed to validate the efficacy of the methods presented.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectClassificationen_US
dc.subjectMachine learningen_US
dc.subjectLogistic regressionen_US
dc.titleDeveloping new methodologies for rank-based classifiersen_US
dc.typeinfo:eu-repo/semantics/masterThesis
dc.typemaster thesisen_US
dc.degree.disciplineStatisticsen_US
dc.contributor.examiningcommitteeJafari-Jozani, Mohammad (Statistics)en_US
dc.contributor.examiningcommitteeTurgeon, Maxime (Statistics)en_US
dc.contributor.examiningcommitteeMuthukumarana, Saman (Statistics)en_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.noteOctober 2022en_US
dc.contributor.guestmembersZhou, Zhiyang (Statistics)en_US
project.funder.nameUniversity of Manitobaen_US
project.funder.identifierhttps://doi.org/10.13039/100010318en_US
local.subject.manitobanoen_US


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