  In this paper, we propose an AdaBoost-assisted extreme learning machine for
efficient online sequential classification (AOS-ELM). In order to achieve
better accuracy in online sequential learning scenarios, we utilize the
cost-sensitive algorithm-AdaBoost, which diversifying the weak classifiers, and
adding the forgetting mechanism, which stabilizing the performance during the
training procedure. Hence, AOS-ELM adapts better to sequentially arrived data
compared with other voting based methods. The experiment results show AOS-ELM
can achieve 94.41% accuracy on MNIST dataset, which is the theoretical accuracy
bound performed by an original batch learning algorithm, AdaBoost-ELM.
Moreover, with the forgetting mechanism, the standard deviation of accuracy
during the online sequential learning process is reduced to 8.26x.
