RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement

by   Bo Han, et al.

Extreme learning machine (ELM) as an emerging branch of shallow networks has shown its excellent generalization and fast learning speed. However, for blended data, the robustness of ELM is weak because its weights and biases of hidden nodes are set randomly. Moreover, the noisy data exert a negative effect. To solve this problem, a new framework called RMSE-ELM is proposed in this paper. It is a two-layer recursive model. In the first layer, the framework trains lots of ELMs in different groups concurrently, then employs selective ensemble to pick out an optimal set of ELMs in each group, which can be merged into a large group of ELMs called candidate pool. In the second layer, selective ensemble is recursively used on candidate pool to acquire the final ensemble. In the experiments, we apply UCI blended datasets to confirm the robustness of our new approach in two key aspects (mean square error and standard deviation). The space complexity of our method is increased to some degree, but the results have shown that RMSE-ELM significantly improves robustness with slightly computational time compared with representative methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP and E-GASEN). It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future.


page 1

page 2

page 3

page 4


LARSEN-ELM: Selective Ensemble of Extreme Learning Machines using LARS for Blended Data

Extreme learning machine (ELM) as a neural network algorithm has shown i...

Rough extreme learning machine: a new classification method based on uncertainty measure

Extreme learning machine (ELM) is a new single hidden layer feedback neu...

Global convergence of Negative Correlation Extreme Learning Machine

Ensemble approaches introduced in the Extreme Learning Machine (ELM) lit...

Robustness Analysis of the Data-Selective Volterra NLMS Algorithm

Recently, the data-selective adaptive Volterra filters have been propose...

Extreme Learning Machine with Local Connections

This paper is concerned with the sparsification of the input-hidden weig...

Robust Sequential Online Prediction with Dynamic Ensemble of Multiple Models: A Concise Introduction

In this paper, I give a concise introduction to a generic theoretical fr...

Apply Ant Colony Algorithm to Search All Extreme Points of Function

To find all extreme points of multimodal functions is called extremum pr...

Please sign up or login with your details

Forgot password? Click here to reset