Abstract：It is problematic to classify data stream with imblanced class distributions for general online learning algorithms, especially in case of concept drift. In this paper, an adaptive weighted online extreme learning machine(AWO-ELM) is developed for imbalance data stream. AWO-ELM is an online learning method and it alleviates the class imbalance problem in chunk-by-chunk learning. Instead of adopting fixed weights, an efficient weight selection strategy is proposed to obtain better classification performance, and thus it can be applied to the task of learning static data stream with different imbalance ratio and the task of online learning with concept drift. The theoretical analysis and experimental results of several real data stream show that AWO-ELM obtains comparable or better classification performance than competing methods.
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2019, Vol. 32 
