Double-coupling learning for multi-task data stream classification

Abstract

Data stream classification methods exploiting cohesion in a single data stream have demonstrated promising performance. However, scenarios involving multiple data streams are indeed common in practice. They involve several correlated data streams and can be viewed as multi-task data streams. Instead of considering them separately as individual data streams, it is beneficial to leverage the correlations among the multi-task data streams in data stream modeling. In this regard, a novel classification method called double-coupling support vector machines (DC-SVM) is proposed to classify multiple data streams simultaneously, where both the external correlations between multiple data streams and the internal relationships within each individual data stream are considered. Experimental results on synthetic and real-world multi-task data streams show that the proposed method outperforms traditional data stream classification methods.