  Data stream classification methods demonstrate promising performance on a
single data stream by exploring the cohesion in the data stream. However,
multiple data streams that involve several correlated data streams are common
in many practical scenarios, which can be viewed as multi-task data streams.
Instead of handling them separately, it is beneficial to consider the
correlations among the multi-task data streams for data stream modeling tasks.
In this regard, a novel classification method called double-coupling support
vector machines (DC-SVM), is proposed for classifying them simultaneously.
DC-SVM considers the external correlations between multiple data streams, while
handling the internal relationship within the individual data stream.
Experimental results on artificial and real-world multi-task data streams
demonstrate that the proposed method outperforms traditional data stream
classification methods.
