Comparative study evaluates concept drift detection methods across real-world data streams, highlighting trade-offs between error-based and window-based detection strategies.
The arrival of data has changed in today's digital environment, becoming more dynamic. Dynamic data is characterized by its speed, variety, and infinite size. Data streams are one category of dynamic data. To address the issues with data streams, several strategies and AI models were developed. One such problem is concept drift, which results from changes in the data's distribution and eventually lowers the performance of the AI model. This means that regular updates to the model are required. In our work, we will analyse the performance using evaluation metrics and compare the effectiveness of the current error-based methods with window-based methods for real-world datasets.
No takes yet. Share an insight, caveat, or question.
S et al. (2024) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: