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Resample-Based Ensemble Framework for Drifting Imbalanced Data Streams
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DOI：10.1109/ACCESS.2019.2914725.png)
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Abstract
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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 classfiers 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.
Keywords:
Online ensemble learning
resample learning
reinforcement
concept drift
class imbalance
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