AdaBoost-assisted Extreme Learning Machine for Efficient Online Sequential Classification
Abstract
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.
Keywords
Cite
@article{arxiv.1909.07115,
title = {AdaBoost-assisted Extreme Learning Machine for Efficient Online Sequential Classification},
author = {Yi-Ta Chen and Yu-Chuan Chuang and An-Yeu and Wu},
journal= {arXiv preprint arXiv:1909.07115},
year = {2019}
}