AdaBoost-Assisted Extreme Learning Machine for Efficient Online Sequential Classification
Journal
IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
Journal Volume
2019-October
Pages
131-136
ISBN
9.78173E+12
Date Issued
2019
Author(s)
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 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. © 2019 IEEE.
Subjects
AdaBoost; cost sensitive learning; forgetting mechanism; Online sequential extreme learning machine
Other Subjects
Adaptive boosting; Knowledge acquisition; Machine learning; Signal processing; Silicon compounds; Cost-sensitive algorithm; Cost-sensitive learning; Extreme learning machine; Forgetting mechanisms; Online sequential extreme learning machine; Sequential learning; Theoretical accuracy; Training procedures; E-learning
Type
conference paper