Automated machine learning for positive-unlabelled learning

Abstract Positive-Unlabelled (PU) learning is a 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. Many PU learning methods have been proposed 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 PU learning Auto-ML systems: BO-Auto-PU, based on a Bayesian Optimisation (BO) approach, and EBO-Auto-PU, based on a novel evolutionary/BO approach. We 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 datasets, each with 3 versions). The results of the comparison show statistically significant improvements in predictive accuracy over the baseline methods, as well as large improvements in computational time for the newly proposed Auto-PU systems over the original Auto-PU system.