GA-Auto-PU: A genetic algorithm-based automated machine learning system for Positive-Unlabeled learning

Positive-Unlabeled (PU) learning is a growing field of machine learning that now consists of numerous algorithms; the number is now so large that considering an extensive manual search to select the best algorithm for a given task is impractical. As such, the area of PU learning could benefit from an Automated Machine Learning (Auto-ML) system, which selects the best algorithm for a given input dataset, among a pre-defined set of candidate algorithms. This work proposes such with GA-Auto-PU, a Genetic Algorithm-based Auto-ML system that can generate PU learning algorithms. Experiments with 20 real-world datasets show that GA-Auto-PU

significantly outperformed a state-of-the-art PU learning method.