Automated machine learning (AutoML) supports the algorithmic construction and data-
specific customization of machine learning pipelines, including the selection, combination …

Investigating strategies that are able to efficiently deal with multi-label classification tasks is
a current research topic in machine learning. Many methods have been proposed, making …

Automated machine learning (AutoML) deals with the automatic composition and
configuration of machine learning pipelines, including the selection and parametrization of …

In multi-label classification a datapoint can be labelled with more than one class at the same
time. A common but trivial approach to multi-label classification is to train individual binary …

Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn,
and more recently ML-Plan, have shown impressive results for the tasks of single-label …

Meta learning generalizes the empirical experience with different learning tasks and holds
promise for providing important empirical insight into the behavior of machine learning …

The idea to exploit label dependencies for better prediction is at the core of methods for multi-
label classification (MLC), and performance improvements are normally explained in this …

In multi-label classification (MLC), each instance is associated with a set of class labels, in
contrast to standard classification, where an instance is assigned a single label. Binary …

In Machine Learning, algorithm choice greatly affects the performance on a problem.
Different advantages and disadvantages have to be taken into account in view of the specific …

Automated Machine Learning is a research area which has gained a lot of focus in the
recent past. But the required components to build an autoML system is neither properly …