  Unsupervised anomaly detection (AD) is critical for a wide range of practical
applications, from network security to health and medical tools. Due to the
diversity of problems, no single algorithm has been found to be superior for
all AD tasks. Choosing an algorithm, otherwise known as the Algorithm Selection
Problem (ASP), has been extensively examined in supervised classification
problems, through the use of meta-learning and AutoML, however, it has received
little attention in unsupervised AD tasks. This research proposes a new
meta-learning approach that identifies an appropriate unsupervised AD algorithm
given a set of meta-features generated from the unlabelled input dataset. The
performance of the proposed meta-learner is superior to the current state of
the art solution. In addition, a mixed model statistical analysis has been
conducted to examine the impact of the meta-learner components: the meta-model,
meta-features, and the base set of AD algorithms, on the overall performance of
the meta-learner. The analysis was conducted using more than 10,000 datasets,
which is significantly larger than previous studies. Results indicate that a
relatively small number of meta-features can be used to identify an appropriate
AD algorithm, but the choice of a meta-model in the meta-learner has a
considerable impact.
