  Classifier chains is a key technique in multi-label classification, since it
allows to consider label dependencies effectively. However, the classifiers are
aligned according to a static order of the labels. In the concept of dynamic
classifier chains (DCC) the label ordering is chosen for each prediction
dynamically depending on the respective instance at hand. We combine this
concept with the boosting of extreme gradient boosted trees (XGBoost), an
effective and scalable state-of-the-art technique, and incorporate DCC in a
fast multi-label extension of XGBoost which we make publicly available. As only
positive labels have to be predicted and these are usually only few, the
training costs can be further substantially reduced. Moreover, as experiments
on eleven datasets show, the length of the chain allows for a more control over
the usage of previous predictions and hence over the measure one want to
optimize.
