Learning in the presence of class imbalance and concept drift

Accepted papers will be published in our Workshop proceeding. The authors will be invited to submit an extended version to the special issue published in Neurocomputing or Connection Science (confirmed). With the wide application of machine learning algorithms to the real world, class imbalance and concept drift in data streams have become crucial learning issues. They can significantly hinder predictive performance, and the problem becomes particularly challenging when they occur simultaneously. The aim of this workshop is to solve the combined issue of class imbalance and concept drift. It is also important to advance the state-of-the art in each individual area.