scalable and efficient multi-label classification for evolving data streams

this paper proposes a new experimental framework for learning and evaluating on multi-label data streams, and uses it to study the performance of various methods. from this study, we develop a multi-label hoeffding tree with multi-label classifiers at the leaves. we show empirically that this method is well suited to this challenging task. using our new framework, which allows us to generate realistic multi-label data streams with concept drift (as well as real data), we compare with a selection of baseline methods, as well as new learning methods from the literature, and show that our hoeffding tree method achieves fast and more accurate performance.