  In recent years, Deep Neural Networks (DNNs) have gained progressive momentum
in many areas of machine learning. The layer-by-layer process of DNNs has
inspired the development of many deep models, including deep ensembles. The
most notable deep ensemble-based model is Deep Forest, which can achieve highly
competitive performance while having much fewer hyper-parameters comparing to
DNNs. In spite of its huge success in the batch learning setting, no effort has
been made to adapt Deep Forest to the context of evolving data streams. In this
work, we introduce the Streaming Deep Forest (SDF) algorithm, a
high-performance deep ensemble method specially adapted to stream
classification. We also present the Augmented Variable Uncertainty (AVU) active
learning strategy to reduce the labeling cost in the streaming context. We
compare the proposed methods to state-of-the-art streaming algorithms in a wide
range of datasets. The results show that by following the AVU active learning
strategy, SDF with only 70\% of labeling budget significantly outperforms other
methods trained with all instances.
