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.
Original language | English |
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Title of host publication | Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI) |
Place of Publication | New York City |
Publisher | AAAI Press |
Pages | 2118-2124 |
Number of pages | 7 |
Publication status | Published - 15 Jul 2016 |