When concept drift occurs within streaming data, a streaming data classification framework needs to update the learning model to maintain its performance. Labeled samples required for training a new model are often unavailable immediately in real world applications. This delay of labels might negatively impact the performance of traditional streaming data classification frameworks. To solve this problem, we propose Sliding Reservoir Approach for Delayed Labeling (SRADL).
Dynamic data streams may contain concept drifts, which are data distribution changes that affect the underlying data model. Different types of concept drifts can occur within a real-world data stream. Majority of current study focuses on detecting one type of drift or detecting drift with labeled data, which is not always available in real-world scenarios. This study focuses on detecting all types of drift with limited labeled samples. Ensemble Framework for Drift Detection (EFDD) is proposed.