Selection-based resampling ensemble algorithm for nonstationary imbalanced stream data learning

Abstract 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 drift is further complicated when the data set is class imbalanced. Meanwhile, most of the existing techniques have ignored the influence of complex data distribution on learning imbalanced data streams. To overcome these issues, we propose an ensemble-based model for learning concept drift from imbalanced data streams with complex data distribution, called selection-based resampling ensemble (SRE). SRE combines the operators of resampling and periodical update to handle the joint issue. In the chunk-based framework, a selection-based resampling mechanism, which focuses on drifting and unsafe examples, is first employed to re-balance the class distribution of the latest block. Then, previous ensemble members are periodically updated using the latest examples, where update weights are determined to emphasize costly misclassification examples and minority examples. Meanwhile, SRE can quickly react to new conditions. Empirical studies demonstrate the effectiveness of SRE in learning nonstationary imbalanced data streams.