Project: Exceptional Model Mining with Missing Data
Description
In this project, we develop an instance of Exceptional Model Mining using the HBSC dataset (together with UU and Trimbos Institute). The HBSC study is repeated every four years among Dutch adolescents and among others, collects information about their drug and alcohol use. We will especially focus on how to handle missing data when doing EMM. The HBSC dataset is a perfect use case for this as it is mostly complete and can be used to artifically generate missing data. The work in this thesis will follow up on earlier work done by Rianne Schouten and Wouter Duivesteijn, together with UU and Trimbos Institute (https://rianneschouten.github.io/pdfs/2022_Schouten_EMMRCS.pdf).
For more information or to see if this project is a match for you, please do not hesitate to reach out: r.m.schouten@tue.nl.
Details
- Supervisor
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Rianne Schouten
- Secondary supervisor
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Wouter Duivesteijn
- Interested?
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