Modeling Multi-Label Recurrence in Data Streams

Most of the existing data stream algorithms assume a single label as the target variable. However, in many applications, each observation is assigned to several labels with latent dependencies among them, which their target function may change over time. Classification of such non-stationary multi-label streaming data with the consideration of dependencies among labels and potential drifts is a challenging task. The few existing studies mostly cope with drifts implicitly, and all learn models on the original label space, which requires a lot of time and memory. None of them consider recurrent drifts in multi-label streams and particularly drifts and recurrences visible in a latent label space. In this paper, we propose a graph-based framework that maintains a pool of multi-label concepts with transitions among them and the corresponding multi-label classifiers. As a base classifier, a fast linear label space dimension reduction method is developed that transforms the labels into a random encoded space and trains models in the reduced space. An analytical method updates the decoding matrix which is used during the test phase to map the labels back into the original space. Experimental results show the effectiveness of the proposed framework in terms of prediction performance and pool management.