SMOClust: Synthetic Minority Oversampling based on Stream Clustering for
  Evolving Data Streams
Chun Wai Chiu Leandro L. Minku
Many real-world data stream applications not only suffer from concept drift
but also class imbalance. Yet, very few existing studies investigated this
joint challenge. Data difficulty factors, which have been shown to be key
challenges in class imbalanced data streams, are not taken into account by
existing approaches when learning class imbalanced data streams. In this work,
we propose a drift adaptable oversampling strategy to synthesise minority class
examples based on stream clustering. The motivation is that stream clustering
methods continuously update themselves to reflect the characteristics of the
current underlying concept, including data difficulty factors. This nature can
potentially be used to compress past information without caching data in the
memory explicitly. Based on the compressed information, synthetic examples can
be created within the region that recently generated new minority class
examples. Experiments with artificial and real-world data streams show that the
proposed approach can handle concept drift involving different minority class
decomposition better than existing approaches, especially when the data stream
is severely class imbalanced and presenting high proportions of safe and
borderline minority class examples.