Classification of multi-class imbalanced data streams using a dynamic data-balancing technique

The performance of classification algorithms with imbalanced streaming data depends upon efficient re-balancing strategy for learning tasks. The difficulty becomes more elevated with multi-class highly imbalanced streaming data. In this paper, we investigate the multi-class imbalance problem in data streams and develop an adaptive framework to cope with imbalanced data scenarios. The proposed One-Vs-All Adaptive Window re-Balancing with Retain Knowledge (OVA-AWBReK) classification framework will combine OVA binarization with Automated Re-balancing Strategy (ARS) using Racing Algorithm (RA). We conducted experiments on highly imbalanced datasets to demonstrate the use of the proposed OVA-AWBReK framework. The results show that OVA-AWBReK framework can enhance the classification performance of the multi-class highly imbalanced data