  Positive-Unlabelled (PU) learning is a growing field of machine learning that
aims to learn classifiers from data consisting of labelled positive and
unlabelled instances, which can be in reality positive or negative, but whose
label is unknown. An extensive number of methods have been proposed to address
PU learning over the last two decades, so many so that selecting an optimal
method for a given PU learning task presents a challenge. Our previous work has
addressed this by proposing GA-Auto-PU, the first Automated Machine Learning
(Auto-ML) system for PU learning. In this work, we propose two new Auto-ML
systems for PU learning: BO-Auto-PU, based on a Bayesian Optimisation approach,
and EBO-Auto-PU, based on a novel evolutionary/Bayesian optimisation approach.
We also present an extensive evaluation of the three Auto-ML systems, comparing
them to each other and to well-established PU learning methods across 60
datasets (20 real-world datasets, each with 3 versions in terms of PU learning
characteristics).
