A Systematic Study of Online Class Imbalance Learning with Concept Drift
It addresses a critical gap for researchers and practitioners in online learning, but is incremental as it synthesizes existing knowledge rather than introducing a new method.
This paper tackles the combined problem of class imbalance and concept drift in data streams by conducting a systematic review and experimental study, proposing a general guideline for effective algorithm development.
As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift. It deals with data streams having very skewed class distributions, where concept drift may occur. It has recently received increased research attention; however, very little work addresses the combined problem where both class imbalance and concept drift coexist. As the first systematic study of handling concept drift in class-imbalanced data streams, this paper first provides a comprehensive review of current research progress in this field, including current research focuses and open challenges. Then, an in-depth experimental study is performed, with the goal of understanding how to best overcome concept drift in online learning with class imbalance. Based on the analysis, a general guideline is proposed for the development of an effective algorithm.