A Machine Learning Approach to Transit Fraud Detection
| Metadata Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Gupta, Ashish | |
| dc.contributor.author | Claiborne, Jerry | |
| dc.date.accessioned | 2022-04-25T12:34:57Z | |
| dc.date.available | 2022-04-25T12:34:57Z | |
| dc.date.issued | 2022-04-25 | |
| dc.identifier.uri | https://etd.auburn.edu//handle/10415/8139 | |
| dc.description.abstract | This 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.rights | EMBARGO_NOT_AUBURN | en_US |
| dc.subject | Systems and Technology | en_US |
| dc.title | A Machine Learning Approach to Transit Fraud Detection | en_US |
| dc.type | PhD Dissertation | en_US |
| dc.embargo.length | MONTHS_WITHHELD:12 | en_US |
| dc.embargo.status | EMBARGOED | en_US |
| dc.embargo.enddate | 2023-04-25 | en_US |
| dc.contributor.committee | Paradice, David | |
| dc.contributor.committee | Hall, Dianne | |
| dc.contributor.committee | Pei, Xu |