Parameter-Free Extreme Learning Machine for Imbalanced Classification Authors Li, L - China Agric

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dc.citation.issue3
dc.citation.volume52
dc.contributor.authorLi L
dc.contributor.authorZhao K
dc.contributor.authorSun R
dc.contributor.authorGan J
dc.contributor.authorYuan G
dc.contributor.authorLiu T
dc.date.available2020-12
dc.date.issued2020-12
dc.descriptionCAUL read and publish agreement 2022
dc.description.abstractImbalanced data distribution is a common problem in classification situations, that is the number of samples in different categories varies greatly, thus increasing the classification difficulty. Although many methods have been used for the imbalanced data classification, there are still problems with low classification accuracy in minority class and adding additional parameter settings. In order to increase minority classification accuracy in imbalanced problem, this paper proposes a parameter-free weighting learning mechanism based on extreme learning machine and sample loss values to balance the number of samples in each training step. The proposed method mainly includes two aspects: the sample weight learning process based on the sample losses; the sample selection process and weight update process according to the constraint function and iterations. Experimental results on twelve datasets from the KEEL repository show that the proposed method could achieve more balanced and accurate results than other compared methods in this work.
dc.description.publication-statusPublished
dc.format.extent1927 - 1944
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000542675100002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifier.citationNEURAL PROCESSING LETTERS, 2020, 52 (3), pp. 1927 - 1944
dc.identifier.doi10.1007/s11063-020-10282-z
dc.identifier.eissn1573-773X
dc.identifier.elements-id433430
dc.identifier.harvestedMassey_Dark
dc.identifier.issn1370-4621
dc.identifier.urihttps://hdl.handle.net/10179/17428
dc.publisherSpringer Science+Business Media, LLC
dc.relation.isPartOfNEURAL PROCESSING LETTERS
dc.subjectParameter-free
dc.subjectExtreme learning machine
dc.subjectClass imbalance problem
dc.subjectG-mean
dc.subject.anzsrc0801 Artificial Intelligence and Image Processing
dc.subject.anzsrc1702 Cognitive Sciences
dc.titleParameter-Free Extreme Learning Machine for Imbalanced Classification Authors Li, L - China Agric
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Mathematical and Computational Sciences
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