Evaluating class imbalance and asymmetric costs using machine learning

dc.contributor.committeeChairLittle, Todd
dc.contributor.committeeMemberLee, Jaehoon
dc.contributor.committeeMemberWang, Eugene
dc.creatorMeeks, Samuel Frank
dc.creator.orcid0000-0001-6722-7021
dc.date.accessioned2020-10-20T16:13:52Z
dc.date.available2020-10-20T16:13:52Z
dc.date.created2020-08
dc.date.issued2020-08
dc.date.submittedAugust 2020
dc.date.updated2020-10-20T16:13:52Z
dc.description.abstractThe current study will evaluate the use of machine learning as a form of risk assessment as it fits within the risk-need-responsivity framework. Specifically, the risk of violent reconviction will attempt to be predicted by multiple machine learning algorithms. As violent reconviction has significant class imbalance, as well as asymmetric error cost, methodologies accounting for these potentially problematic situations will be evaluated. While machine learning has been shown as an improvement over traditional assessment, more research is necessary to determine the most effective practices when applying its specialized methodologies. Analysis of the techniques used as treatment for class imbalance and asymmetric cost has not been researched on actual criminal justice data, leading to a gap in the scientific literature necessary to evaluate their genuine performance when applied.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2346/86573
dc.language.isoeng
dc.rights.availabilityUnrestricted.
dc.subjectMachine learning
dc.subjectAsymmetric cost
dc.subjectClass imbalance
dc.subjectCriminal justice
dc.titleEvaluating class imbalance and asymmetric costs using machine learning
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentEducational Psychology and Leadership
thesis.degree.disciplineEducational Psychology
thesis.degree.grantorTexas Tech University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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