Ature, Angbera (2025) A Sliding Adaptive Beta Distribution Model For Concept Drift Detection In A Dynamic Environment. PhD thesis, Universiti Sains Malaysia.
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Abstract
Machine learning models deployed in data streaming environments often suffer from concept drift, where the underlying data distribution changes over time, leading to performance degradation. Detecting and adapting to these shifts in real time is crucial to maintaining model accuracy and reliability. This study introduces the Sliding Adaptive Beta Distribution Model (SABeDM), a novel approach for concept drift detection and adaptation in dynamic data streams.
| Item Type: | Thesis (PhD) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science |
| Divisions: | Pusat Pengajian Sains Komputer (School of Computer Sciences) > Thesis |
| Depositing User: | Mr Aizat Asmawi Abdul Rahim |
| Date Deposited: | 09 Mar 2026 08:25 |
| Last Modified: | 09 Mar 2026 08:25 |
| URI: | http://eprints.usm.my/id/eprint/63748 |
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