An Unsupervised Drift Detector For Online Imbalanced Evolving Streams

Detecting concept drift from an imbalanced evolving stream is challenging task. At high degree of imbalance ratios, the poor or nil performance estimates of the learner from minority class tends to drift detection failures. To ameliorate this problem, we propose a new drift detection and adaption framework. Proposed drift detection mechanism is carried out in two phases includes unsupervised and supervised drift detection with queried labels. The adaption framework is based on the batch wise active learning. Comparative results on four synthetic and one real world balanced and imbalanced evolving streams with other prominent drift detection methods indicates that our approach is better in detecting the drift with low false positive rates.