Evolving machine learning workflows through interactive AutoML
Rafael Barbudo Aurora Ramírez José Raúl Romero
Automatic workflow composition (AWC) is a relevant problem in automated
machine learning (AutoML) that allows finding suitable sequences of
preprocessing and prediction models together with their optimal
hyperparameters. This problem can be solved using evolutionary algorithms and,
in particular, grammar-guided genetic programming (G3P). Current G3P approaches
to AWC define a fixed grammar that formally specifies how workflow elements can
be combined and which algorithms can be included. In this paper we present
\ourmethod, an interactive G3P algorithm that allows users to dynamically
modify the grammar to prune the search space and focus on their regions of
interest. Our proposal is the first to combine the advantages of a G3P method
with ideas from interactive optimisation and human-guided machine learning, an
area little explored in the context of AutoML. To evaluate our approach, we
present an experimental study in which 20 participants interact with \ourmethod
to evolve workflows according to their preferences. Our results confirm that
the collaboration between \ourmethod and humans allows us to find
high-performance workflows in terms of accuracy that require less tuning time
than those found without human intervention.