Ensembling Extreme Learning Machines

Authors:
Huawei Chen;Huahong Chen;Xiaoling Nian;Peipei Liu
Affiliations:
School of Information Science & Technology, Southwest Jiaotong University, P.O. Box 406, Chengdu, Sichuan 610031, China;School of Economics & Management, Southwest Jiaotong University, Chengdu, Sichuan 610031, China;School of Information Science & Technology, Southwest Jiaotong University, P.O. Box 406, Chengdu, Sichuan 610031, China;School of Information Science & Technology, Southwest Jiaotong University, P.O. Box 406, Chengdu, Sichuan 610031, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

Extreme learning machine (ELM) is a novel learning algorithm much faster than the traditional gradient-based learning algorithms for single-hidden-layer feedforward neural networks (SLFNs). Neural network ensemble is a learning paradigm where several neural networks are jointly used to solve a problem. In our work, we investigated the performance of ELMs ensemble on regression problems. A simple ensembling approach Product Index based Excluding ensemble(PIEx) was proposed to ensemble accurate and diverse member networks. The experimental results show that the ensemble can effectively improve the performance compared with the generalization ability of single ELM and PIEx outperforms Bagging and Simple Averaging. The results also show ELM training can generate diverse neural networks even though using the same training set.