Autonomic active learning strategy using cluster-based ensemble classifier for concept drifts in imbalanced data stream

Abstract

Systems are becoming autonomous nowadays. In real-world scenarios, the involvement of experts in generating a new decision has to be paid a certain amount of cost. Thus, in these conditions, creating a system with the least expert involvement and costs is the main destination. As a solution, an autonomic active learning strategy with a cluster-based ensemble classifier for concept drifts in imbalanced data stream (AACE-DI) is proposed in this paper. Initially, a cluster-based initializing formula is considered to select the most informative instances for labeling a dynamically changing ensemble classifier. Next, the automatically adjusting uncertainty strategy is introduced to prioritize the most uncertain, representative, and minority class data from imbalanced data stream, which also ensures the least labeling cost. This automatic formula not only outperforms the system but also makes it autonomic. Experimental evaluations are organized using 6 real-world data streams and 16 synthetic data streams with different types of concept drift and imbalanced ratios. To measure performance, evaluators, such as the prequential area under curve, recall, and geometric mean, are considered. Moreover, the proposed AACE-DI method achieves expected outcomes with the least cost as compared with other state-of-the-art learning methods.