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Many real-world data stream applications not only suffer from concept driftbut also class imbalance. Yet, very few existing studies investigated thisjoint challenge. Data difficulty factors, which have been shown to be keychallenges in class imbalanced data streams, are not taken into account byexisting approaches when learning class imbalanced data streams. In this work,we propose a drift adaptable oversampling strategy to synthesise minority classexamples based on stream clustering. The motivation is that stream clusteringmethods continuously update themselves to reflect the characteristics of thecurrent underlying concept, including data difficulty factors. This nature canpotentially be used to compress past information without caching data in thememory explicitly. Based on the compressed information, synthetic examples canbe created within the region that recently generated new minority classexamples. Experiments with artificial and real-world data streams show that theproposed approach can handle concept drift involving different minority classdecomposition better than existing approaches, especially when the data streamis severely class imbalanced and presenting high proportions of safe andborderline minority class examples.

SMOClust: Synthetic Minority Oversampling based on Stream Clustering for Evolving Data Streams