Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 808292, 7 pages
Research Article

Evolutionary Voting-Based Extreme Learning Machines

1Department of Emergency Medicine, Singapore General Hospital, Singapore 169608
2Institute of Information and Control, Hangzhou Dianzi University, Zhejiang 310018, China
3School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798
4Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore 169857

Received 13 June 2014; Accepted 29 July 2014; Published 14 August 2014

Academic Editor: Tao Chen

Copyright © 2014 Nan Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Voting-based extreme learning machine (V-ELM) was proposed to improve learning efficiency where majority voting was employed. V-ELM assumes that all individual classifiers contribute equally to the decision ensemble. However, in many real-world scenarios, this assumption does not work well. In this paper, we aim to enhance V-ELM by introducing weights to distinguish the importance of each individual ELM classifier in decision making. Genetic algorithm is used for optimizing these weights. This evolutionary V-ELM is named as EV-ELM. Results on several benchmark databases show that EV-ELM achieves the highest classification accuracy compared with V-ELM and ELM.