Please use this identifier to cite or link to this item: https://doi.org/10.1155/2014/808292
DC FieldValue
dc.titleEvolutionary voting-based extreme learning machines
dc.contributor.authorLiu, N
dc.contributor.authorCao, J
dc.contributor.authorLin, Z
dc.contributor.authorPek, P.P
dc.contributor.authorKoh, Z.X
dc.contributor.authorOng, M.E.H
dc.date.accessioned2020-10-26T07:19:06Z
dc.date.available2020-10-26T07:19:06Z
dc.date.issued2014
dc.identifier.citationLiu, 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
dc.identifier.issn1024-123X
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/180165
dc.description.abstractVoting-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.
dc.publisherHindawi Publishing Corporation
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20201031
dc.subjectExtreme learning machine
dc.typeArticle
dc.contributor.departmentDUKE-NUS MEDICAL SCHOOL
dc.description.doi10.1155/2014/808292
dc.description.sourcetitleMathematical Problems in Engineering
dc.description.volume2014
dc.description.page808292
dc.published.statePublished
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