Robust OS-ELM with a novel selective ensemble based on particle swarm optimization

TL;DR

This paper introduces a robust online sequential extreme learning machine (ROS-ELM) that employs a novel particle swarm optimization-based selective ensemble to enhance robustness and stability in online learning tasks.

Contribution

It proposes a new PSOSEN algorithm and an adaptive ensemble framework applicable to various learning algorithms, improving robustness in online learning.

Findings

ROS-ELM outperforms OS-ELM and EOS-ELM in robustness and stability

Experimental results on UCI datasets validate the effectiveness of the proposed method

The PSOSEN algorithm is versatile for different learning paradigms

Abstract

In this paper, a robust online sequential extreme learning machine (ROS-ELM) is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. First, a novel selective ensemble algorithm referred to as particle swarm optimization selective ensemble (PSOSEN) is proposed. Noting that PSOSEN is a general selective ensemble method which is applicable to any learning algorithms, including batch learning and online learning. Second, an adaptive selective ensemble framework for online learning is designed to balance the robustness and complexity of the algorithm. Experiments for both regression and classification problems with UCI data sets are carried out. Comparisons between OS-ELM, simple ensemble OS-ELM (EOS-ELM) and the proposed ROS-ELM empirically show that ROS-ELM significantly improves the robustness and stability.