Online class imbalance learning constitutes a new problem and an emerging research topic that focusses on the challenges of online learning under class imbalance and concept drift. Class imbalance deals with data streams that have very skewed distributions while concept drift deals with changes in the class imbalance status. Little work exists that addresses these challenges and in this paper we introduce queue-based resampling (QBR), a novel algorithm that successfully addresses the co-existence of class imbalance and concept drift.
You can download the paper from here. Please cite our work as follows:
K.Malialis, C.Panayiotou and M.M. Polycarpou, Queue-based resampling for online class imbalance learning, in Proceedings of the 27th International Conference on Artificial Neural Networks (ICANN), 2018.
For any questions or issues please contact Kleanthis Malialis.