AN-YEU(ANDY) WU2022-05-192022-05-1920199.78173E+1215206130https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082390760&doi=10.1109%2fSiPS47522.2019.9020609&partnerID=40&md5=7fe1d9a35a2e321eb1f1db23910da48ahttps://scholars.lib.ntu.edu.tw/handle/123456789/611223In 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.AdaBoost; cost sensitive learning; forgetting mechanism; Online sequential extreme learning machineAdaptive 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-learningconference paper10.1109/SiPS47522.2019.90206092-s2.0-85082390760