Online Automated Machine Learning for Class Imbalanced Data Streams

Automated machine learning (AutoML) has achieved great success in offline class imbalance learning where data are static. However, many real world applications data nowadays tend to evolve over time in the form of data streams and involve class imbalance distributions, e.g., intrusion detection, fault diagnosis systems, and fraud detection. These learning tasks require AutoML processing the instances instantly and adapting to the dynamic data changes. Nevertheless, existing AutoML research either only focuses on class imbalance in static data sets, or discusses data streams with concept drift. No existing work studied the joint learning challenges of class imbalance and online data stream learning in AutoML. To close the gap, this paper focuses on learning dynamic data streams with a skewed class distribution in AutoML. In this paper, we propose two new AutoML approaches, UEvoAutoML and OEvoAutoML, which integrate adaptive resampling techniques into an existing online AutoML framework. Their performance is investigated through a set of synthetic imbalanced data streams under various stationary and non-stationary scenarios and 5 real-world data streams. As the pioneering work of exploring how class imbalance techniques benefit online AutoML, this paper demonstrated that the effectiveness of adaptive resampling in AutoML frameworks.