AdaBoost-assisted Extreme Learning Machine for Efficient Online Sequential Classification

TL;DR

This paper introduces AOS-ELM, an online classification method combining AdaBoost and extreme learning machines, which improves accuracy and stability in sequential data learning.

Contribution

It presents a novel AdaBoost-assisted ELM with a forgetting mechanism for enhanced online sequential classification performance.

Findings

Achieves 94.41% accuracy on MNIST dataset

Reduces accuracy standard deviation to 8.26 times

Outperforms existing voting-based methods in online learning

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.