Resample Based Ensemble Framework for Drifting Imbalanced Data Streams
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 Imbalanced Stream (RE DI) The ensemble framework consists of a long term static classifier to handle gradual and multiple dynamic classifiers to handle sudden concept drift. The weights of the ensemble classifier are adjusted from two aspects. First, a time decayed strategy decreases the weights of the dynamic classifiers to make the ensemble classifier focus more on the new concept of the data stream. Second, a novel reinforcement mechanism is proposed to increase the weights of the base classifiers that perform better on the minority class and decrease the weights of the classifiers that perform worse. A resampling buffer is used for storing the instances of the minority class to balance the imbalanced distribution over time. In our experiment, we compare the proposed method with other state of the art algorithms on both real world and synthetic data streams. The results show that the proposed method achieves the best performance in terms of both Prequential AUC and accuracy.
IEEE Access