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http://hdl.handle.net/11375/27330
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DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Chiang, Fei | - |
dc.contributor.author | Do, Ethan Quoc-Nam | - |
dc.date.accessioned | 2022-01-31T02:07:47Z | - |
dc.date.available | 2022-01-31T02:07:47Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://hdl.handle.net/11375/27330 | - |
dc.description.abstract | Data drifts naturally occur in data streams due to seasonality, change in data usage, and the data generation process. Concepts modelled via the data streams will also experience such drift. The problem of differentiating concept drift from anomalies is important to identify normal vs abnormal behaviour. Existing techniques achieve poor responsiveness and accuracy towards this differentiation task. We take two approaches to address this problem. First, we extend an existing sliding window algorithm to include multiple windows to model recently seen data stream patterns, and define new parameters to compare the data streams. Second, we study a set of optimisers and tune a Bi-LSTM model parameters to maximize accuracy. | en_US |
dc.language.iso | en | en_US |
dc.subject | concept drift | en_US |
dc.subject | anomaly detection | en_US |
dc.subject | concept drift detection | en_US |
dc.title | Tuning and Optimising Concept Drift Detection | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Computing and Software | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Master of Applied Science (MASc) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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do_ethan_quoc-nam_202112_masc.pdf | 4.14 MB | Adobe PDF | View/Open |
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