Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach

摘要

Difficulties of learning from nonstationary data stream are generally twofold. First, dynamically structured learning framework is required to catch up with the evolution of unstable class concepts, i.e., concept drifts. Second, imbalanced class distribution over data stream demands a mechanism to intensify the underrepresented class concepts for improved overall performance. To alleviate the challenges brought by these issues, we propose the recursive ensemble approach (REA) in this paper. To battle against the imbalanced learning problem in training data chunk received at any timestamp t, i.e.,