  The deployment of Machine Learning (ML) models is a difficult and
time-consuming job that comprises a series of sequential and correlated tasks
that go from the data pre-processing, and the design and extraction of
features, to the choice of the ML algorithm and its parameterisation. The task
is even more challenging considering that the design of features is in many
cases problem specific, and thus requires domain-expertise. To overcome these
limitations Automated Machine Learning (AutoML) methods seek to automate, with
few or no human-intervention, the design of pipelines, i.e., automate the
selection of the sequence of methods that have to be applied to the raw data.
These methods have the potential to enable non-expert users to use ML, and
provide expert users with solutions that they would unlikely consider. In
particular, this paper describes AutoML-DSGE - a novel grammar-based framework
that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of
Scikit-Learn classification pipelines. The experimental results include
comparing AutoML-DSGE to another grammar-based AutoML framework, Resilient
ClassificationPipeline Evolution (RECIPE), and show that the average
performance of the classification pipelines generated by AutoML-DSGE is always
superior to the average performance of RECIPE; the differences are
statistically significant in 3 out of the 10 used datasets.
