Proceedings

Dealing with Multiple Classes in Online Class Imbalance Learning

Shuo Wang, Leandro L. Minku, Xin Yao

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Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2118-2123.

Abstract

Online class imbalance learning deals with data streams having very skewed class distributions in a timely fashion. Although a few methods have been proposed to handle such problems, most of them focus on two-class cases. Multi-class imbalance imposes additional challenges in learning. This paper studies the combined challenges posed by multi-class imbalance and online learning, and aims at a more effective and adaptive solution. First, we introduce two resampling-based ensemble methods, called MOOB and MUOB, which can process multi-class data directly and strictly online with an adaptive sampling rate. Then, we look into the impact of multi-minority and multi-majority cases on MOOB and MUOB in comparison to other methods under stationary and dynamic scenarios. Both multi-minority and multi-majority make a negative impact. MOOB shows the best and most stable G-mean in most stationary and dynamic cases.

Shuo Wang, Leandro L. Minku, and Xin Yao. 2016. Dealing with Multiple Classes in Online Class Imbalance Learning. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 2118–2123. https://ijcai.org/proceedings/2016/302
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