  Automated machine learning (AutoML) has received increasing attention in the
recent past. While the main tools for AutoML, such as Auto-WEKA, TPOT, and
auto-sklearn, mainly deal with single-label classification and regression,
there is very little work on other types of machine learning tasks. In
particular, there is almost no work on automating the engineering of machine
learning applications for multi-label classification. This paper makes two
contributions. First, it discusses the usefulness and feasibility of an AutoML
approach for multi-label classification. Second, we show how the scope of
ML-Plan, an AutoML-tool for multi-class classification, can be extended towards
multi-label classification using MEKA, which is a multi-label extension of the
well-known Java library WEKA. The resulting approach recursively refines MEKA's
multi-label classifiers, which sometimes nest another multi-label classifier,
up to the selection of a single-label base learner provided by WEKA. In our
evaluation, we find that the proposed approach yields superb results and
performs significantly better than a set of baselines.
