Hyperon: An Online Hyperparameter Tuning Approach for Data Stream Learning

Predictive models built using machine learning algorithms usually involve a number of hyperparameters that can significantly affect their performance. While many approaches for hyperparameter tuning have been investigated for offline learning, there is little work in the context of online data stream learning. Hyperparameter tuning for online data stream learning can be particularly challenging, due to possible changes in the underlying distribution of the problem. Such changes can result in the best hyperparameter choice varying over time, requiring efficient, real-time online adaptation. However, existing online hyperparameter tuning approaches are limited to specific models, are susceptible to local optima, rely on fixed hyperparameter grids, or on concept drift detection methods. We propose a novel online hyperparameter tuning approch for data stream learning called Hyperon to overcome these issues. Hyperon intertwines online data stream learning with a steady-state evolutionary algorithm, enabling efficient and effective hyperparameter optimisation over time. Experiments on 10 real world data streams show that Hyperon is able to significantly improve the predictive performance of the underlying online data stream learning approach in a computationally efficient manner.