Please use this identifier to cite or link to this item: https://doi.org/10.1155/2014/808292
Title: Evolutionary voting-based extreme learning machines
Authors: Liu, N 
Cao, J
Lin, Z
Pek, P.P
Koh, Z.X
Ong, M.E.H 
Keywords: Extreme learning machine
Issue Date: 2014
Publisher: Hindawi Publishing Corporation
Citation: Liu, N, Cao, J, Lin, Z, Pek, P.P, Koh, Z.X, Ong, M.E.H (2014). Evolutionary voting-based extreme learning machines. Mathematical Problems in Engineering 2014 : 808292. ScholarBank@NUS Repository. https://doi.org/10.1155/2014/808292
Rights: Attribution 4.0 International
Abstract: 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. © 2014 Nan Liu et al.
Source Title: Mathematical Problems in Engineering
URI: https://scholarbank.nus.edu.sg/handle/10635/180165
ISSN: 1024-123X
DOI: 10.1155/2014/808292
Rights: Attribution 4.0 International
Appears in Collections:Staff Publications
Elements

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1155_2014_808292.pdf2.02 MBAdobe PDF

OPEN

NoneView/Download

SCOPUSTM   
Citations

3
checked on Jul 17, 2021

Page view(s)

68
checked on Jul 22, 2021

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons