  Automated machine learning (AutoML) aims to select and configure machine
learning algorithms and combine them into machine learning pipelines tailored
to a dataset at hand. For supervised learning tasks, most notably binary and
multinomial classification, aka single-label classification (SLC), such AutoML
approaches have shown promising results. However, the task of multi-label
classification (MLC), where data points are associated with a set of class
labels instead of a single class label, has received much less attention so
far. In the context of multi-label classification, the data-specific selection
and configuration of multi-label classifiers are challenging even for experts
in the field, as it is a high-dimensional optimization problem with multi-level
hierarchical dependencies. While for SLC, the space of machine learning
pipelines is already huge, the size of the MLC search space outnumbers the one
of SLC by several orders.
  In the first part of this thesis, we devise a novel AutoML approach for
single-label classification tasks optimizing pipelines of machine learning
algorithms, consisting of two algorithms at most. This approach is then
extended first to optimize pipelines of unlimited length and eventually
configure the complex hierarchical structures of multi-label classification
methods. Furthermore, we investigate how well AutoML approaches that form the
state of the art for single-label classification tasks scale with the increased
problem complexity of AutoML for multi-label classification.
  In the second part, we explore how methods for SLC and MLC could be
configured more flexibly to achieve better generalization performance and how
to increase the efficiency of execution-based AutoML systems.
