A Prototype-driven Framework for Change Detection in Data Stream Classification

This paper presents a prototype-driven framework for classifying evolving data streams. Our framework uses cluster prototypes to summarize the data and to determine whether the current model is outdated. This strategy of rebuilding the model only when significant changes are detected helps to reduce the computational overhead and the amount of labeled examples needed. To improve its accuracy, we also propose a selective sampling strategy to acquire more labeled examples from regions where the model's predictions are unreliable. Our experimental results demonstrate the effectiveness of the proposed framework, both in terms of reducing the amount of model updates and maintaining high accuracy