Learning nonstationary data streams has been well studied in recent years. However, most
of the researches assume that the class imbalance of data streams is relatively balanced …

Abstract Concept drifts occurring in data streams will jeopardize the accuracy and stability of
the online learning process. If the data stream is imbalanced, it will be even more …

Although the issues of concept drift and class imbalance have been studied separately, the
joint problem is underexplored even though it has received increasing attention. Concept …

One of the most challenging problems in the field of online learning is concept drift, which
deeply influences the classification stability of streaming data. If the data stream is …

Ensemble learning, as a popular method to tackle concept drift in data stream, forms a
combination of base classifiers according to their global performances. However, concept …

Classifying non-stationary and imbalanced data streams encompasses two important
challenges, namely concept drift and class imbalance. Concept drift is changes in the …

Machine learning in real-world scenarios is often challenged by concept drift and class
imbalance. This paper proposes a Resample-based Ensemble Framework for Drifting …

Difficulties of learning from nonstationary data stream are generally twofold. First,
dynamically structured learning framework is required to catch up with the evolution of …

Applications challenged by the joint problem of concept drift and class imbalance are
attracting increasing research interest. This paper proposes a novel Reinforcement Online …

Abstract Knowledge extraction from data streams has received increasing interest in recent
years. However, most of the existing studies assume that the class distribution of data …