  Multi-output inference tasks, such as multi-label classification, have become
increasingly important in recent years. A popular method for multi-label
classification is classifier chains, in which the predictions of individual
classifiers are cascaded along a chain, thus taking into account inter-label
dependencies and improving the overall performance. Several varieties of
classifier chain methods have been introduced, and many of them perform very
competitively across a wide range of benchmark datasets. However, scalability
limitations become apparent on larger datasets when modeling a fully-cascaded
chain. In particular, the methods' strategies for discovering and modeling a
good chain structure constitutes a mayor computational bottleneck. In this
paper, we present the classifier trellis (CT) method for scalable multi-label
classification. We compare CT with several recently proposed classifier chain
methods to show that it occupies an important niche: it is highly competitive
on standard multi-label problems, yet it can also scale up to thousands or even
tens of thousands of labels.
