A Multistream Concept Drift Handling Framework via Data Sharing

Bin Zhang, Jie Lu, Yiliao Song, Guangquan Zhang

Abstract: A frequent problem in data stream mining is concept drift, meaning the data distribution changes over time. A common issue when dealing with concept drift is insufficient data. Real-world applications of data stream mining often involve multiple data streams. However, most concept drift methods handle these data streams separately. This study uses data from other data streams to handle the problem of insufficient data. We propose a novel Multistream Concept Drift Handling Framework via data sharing, containing a fuzzy membership-based drift detection (FMDD) component and a fuzzy membership-based drift adaptation (FMDA) component, to train the new learning model for drifting streams by sharing weighted data from other nondrifting streams. A stream fuzzy set is defined with membership functions that measure the degree to which samples belong to a data stream. Our Concept Drift Handling Framework can detect when and in which stream concept drift occurs, and therefore the insufficient data issue can be solved by adding the weighted data from nondrifting streams to train new learning models. Synthetic and real-world experimental results show that our method can help avoid the insufficient data issue and thereby significantly improve the prediction performance.

External IDs:doi:10.1109/tcyb.2025.3605663