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Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61442
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dc.contributor.authorWang, Chaoqunen
dc.contributor.authorHu, Zhongyien
dc.contributor.authorChiong, Raymonden
dc.contributor.authorDhakal, Sandeepen
dc.contributor.authorCheng, Xuanen
dc.contributor.authorBao, Yukunen
dc.date.accessioned2024-07-10T01:04:13Z-
dc.date.available2024-07-10T01:04:13Z-
dc.date.issued2018-
dc.identifier.citationICNC-FSKD 2018 - 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, p. 412-418en
dc.identifier.isbn9781538680971en
dc.identifier.isbn9781538680964en
dc.identifier.isbn9781538680988en
dc.identifier.urihttps://hdl.handle.net/1959.11/61442-
dc.description.abstract<p>We propose a multi-classifier ensemble model based on particle swarm optimization (PSO) for the evaluation of personal credit risk in peer-to-peer (P2P) lending platforms. In the proposed method, we consider the differences and complementarity of the base classifiers' performance and use PSO to optimize their weights. Experimental results show that our proposed P2P personal credit scoring model outperforms both single and other benchmark ensemble models. Among the examined model variants, the ensemble model based on PSO with 100 particles is the best.</p>en
dc.languageenen
dc.publisherIEEEen
dc.relation.ispartofICNC-FSKD 2018 - 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discoveryen
dc.titleA PSO-based ensemble model for peer-to-peer credit scoringen
dc.typeConference Publicationen
dc.relation.conferenceICNC-FSKD 2018: 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discoveryen
dc.identifier.doi10.1109/FSKD.2018.8687154en
local.contributor.firstnameChaoqunen
local.contributor.firstnameZhongyien
local.contributor.firstnameRaymonden
local.contributor.firstnameSandeepen
local.contributor.firstnameXuanen
local.contributor.firstnameYukunen
local.profile.schoolSchool of Science & Technologyen
local.profile.emailrchiong@une.edu.auen
local.output.categoryE1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.date.conference28th - 30th July, 2018en
local.conference.placeHuangshan, Chinaen
local.publisher.placeUnited States of Americaen
local.format.startpage412en
local.format.endpage418en
local.peerreviewedYesen
local.contributor.lastnameWangen
local.contributor.lastnameHuen
local.contributor.lastnameChiongen
local.contributor.lastnameDhakalen
local.contributor.lastnameChengen
local.contributor.lastnameBaoen
dc.identifier.staffune-id:rchiongen
local.profile.orcid0000-0002-8285-1903en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/61442en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleA PSO-based ensemble model for peer-to-peer credit scoringen
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.conference.detailsICNC-FSKD 2018: 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, Huangshan, China, 28th - 30th July, 2018en
local.search.authorWang, Chaoqunen
local.search.authorHu, Zhongyien
local.search.authorChiong, Raymonden
local.search.authorDhakal, Sandeepen
local.search.authorCheng, Xuanen
local.search.authorBao, Yukunen
local.uneassociationNoen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2018en
local.subject.for20204602 Artificial intelligenceen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.date.moved2024-08-29en
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