Online Oversampling for Sparsely Labeled Imbalanced and Non-Stationary Data Streams

Abstract

Learning from imbalanced data and data stream mining are among most popular areas in contemporary machine learning. There is a strong interplay between these domains, as data streams are frequently characterized by skewed distributions. However, most of existing works focus on binary problems, omitting significantly more challenging multi-class imbalanced data. In this paper, we propose a novel framework for learning from multi-class imbalanced data streams that simultaneously tackles three major problems in this area: (i) changing imbalance ratios among multiple classes; (ii) concept drift; and (iii) limited access to ground truth. We use active learning combined with streaming-based oversampling that uses both information about current class ratios and classifier errors on each class to create new instances in a meaningful way. Conducted experimental study shows that our single-classifier framework is capable of outperforming state-of-the-art ensembles dedicated to multi-class imbalanced data streams in both fully supervised and sparsely labeled learning scenarios.