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A Machine Learning Approach to Transit Fraud Detection


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dc.contributor.advisorGupta, Ashish
dc.contributor.authorClaiborne, Jerry
dc.date.accessioned2022-04-25T12:34:57Z
dc.date.available2022-04-25T12:34:57Z
dc.date.issued2022-04-25
dc.identifier.urihttps://etd.auburn.edu//handle/10415/8139
dc.description.abstractThis research is a collection of 3 papers on the use of machine learning methods to detect and classify transit media fraud using passenger transaction data. Academically, this work is an extension of machine learning research into the largely unexplored area of transit media fraud. The implication for industry, is a series of tested and highly effective methods of fraud detection that can be implemented to mitigate the millions of dollars lost in transit fraud each year.en_US
dc.rightsEMBARGO_NOT_AUBURNen_US
dc.subjectSystems and Technologyen_US
dc.titleA Machine Learning Approach to Transit Fraud Detectionen_US
dc.typePhD Dissertationen_US
dc.embargo.lengthMONTHS_WITHHELD:12en_US
dc.embargo.statusEMBARGOEDen_US
dc.embargo.enddate2023-04-25en_US
dc.contributor.committeeParadice, David
dc.contributor.committeeHall, Dianne
dc.contributor.committeePei, Xu

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