Study of anomaly detection in diverse populations using probabilistic graphical models

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dc.contributor.authorTarume, Isaac
dc.date.accessioned2021-07-03T10:28:57Z
dc.date.available2021-07-03T10:28:57Z
dc.date.issued2020
dc.descriptionA thesis submitted to the Faculty of Science, University of the Witwatersrand, in fulfilment of the requirements for the degree of Master of Science, School of Computer Science and Applied Mathematics, 2020en_ZA
dc.description.abstractMost credit card transactions are characterised by randomly changing patterns of behavioural characteristics of the card users involved. The behavioural features tend to vary greatly due to the diversity inherent in most populations. In order to detect fraud and anomalies efficiently in these typical diverse populations using machine learning models, the models must be robust enough to capture the complex user behaviours in their variety. To address these challenges, we used base Hidden Markov Models (HMMs) and then a more richly expressive model called hierarchical Hidden Markov Model (HHMM) that can capture more dimensions and latent variables, which enables it to perform better. Furthermore, the model also reduces the learning times, which is very important when it comes to evaluating diverse population domain such as online credit card transactions as these impacts the response times. We evaluate the performance of HHMM in both individual and diverse population-based credit card transaction anomalies and labelled real-world timeseries dataen_ZA
dc.description.librarianCK2021en_ZA
dc.facultyFaculty of Scienceen_ZA
dc.identifier.urihttps://hdl.handle.net/10539/31418
dc.language.isoenen_ZA
dc.schoolSchool of Computer Science and Applied Mathematicsen_ZA
dc.titleStudy of anomaly detection in diverse populations using probabilistic graphical modelsen_ZA
dc.typeThesisen_ZA
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