Cost-Sensitive Weighting and Imbalance-Reversed Bagging for Streaming Imbalanced and Concept Drifting in Electricity Pricing Classification

In data streaming environments such as smart grid, it is impossible to restrict arriving data to have the same number of samples in each class. Hence, in addition to concept drift, classification problems in streaming data environments are inherently imbalanced. However, these problems in smart grid have rarely been studied. Incremental learning aims to learn correct classification for future unseen samples from the given streaming data. In this work, we propose a new incremental ensemble learning method to handle both issues. The class imbalance issue is tackled by an imbalance-reversed bagging method while the adaptation to concept drift is achieved by a dynamic cost-sensitive weighting scheme for component classifiers. The proposed methodology is applied to a case study for the electricity pricing in New South Wales and Victoria, Australia. Experimental results show the effectiveness of the proposed algorithm with statistical significance in comparison to the state-of-the-art incremental learning methods.