Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/87225
DC FieldValueLanguage
dc.contributor.authorRibeiro, Bernardete Martins-
dc.contributor.authorChen, Ning-
dc.date.accessioned2019-07-02T15:37:11Z-
dc.date.available2019-07-02T15:37:11Z-
dc.date.issued2017-
dc.identifier.issn1875-8843 (E)pt
dc.identifier.issn1872-4981 (P)pt
dc.identifier.urihttp://hdl.handle.net/10316/87225-
dc.description.abstractWith the increasing amount of financial data produced today, the problem of finding the k-nearest neighbors to the query point in high-dimensional space is itself of importance to access the financial credit risk. Binary embeddings are efficient tools of indexing big datasets for financial credit risk analysis. The idea is to find a good hash function such that similar data points in Euclidean space preserve their similarities in the Hamming space for fast data retrieval. By exploring out-of-sample extension to test data it is possible to set forth a go-forward strategy to establish a fast retrieval model of companies' status thereby rendering the stakeholders' evaluation task very efficiently. First, we use semi-supervised learning-based hashing to take into account the pairwise information for constructing the weight adjacency graph matrix needed or building the binarised Laplacian EigenMap. Second, we train a generalised regression neural network (GRNN) to learn the k-bits hash function. Third, the k-bit binary code for the test data is efficiently found in the recall phase. Experimental results on financial data demonstrated the proposed approach showed the applicability and advantages of learning-based hashing to credit risk assessment.pt
dc.language.isoengpt
dc.publisherIOS Presspt
dc.rightsembargoedAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectHashing methodpt
dc.subjectFinancial credit riskpt
dc.subjectGeneralised regression neural networkpt
dc.subjectBinary embeddingpt
dc.subjectK-bits codept
dc.titleFinancial credit risk assessment via learning-based hashingpt
dc.typearticle-
degois.publication.firstPage177pt
degois.publication.lastPage186pt
degois.publication.issue2pt
degois.publication.locationAmsterdampt
degois.publication.titleIntelligent Decision Technologiespt
dc.relation.publisherversionhttps://content.iospress.com/articles/intelligent-decision-technologies/idt286pt
dc.peerreviewedyespt
dc.identifier.doi10.3233/IDT-170286pt
degois.publication.volume11pt
dc.date.embargo2019-11-17*
uc.date.periodoEmbargo1050pt
uc.controloAutoridadeSim-
item.grantfulltextopen-
item.languageiso639-1en-
item.fulltextCom Texto completo-
crisitem.author.deptFaculdade de Ciências e Tecnologia, Universidade de Coimbra-
crisitem.author.parentdeptUniversidade de Coimbra-
crisitem.author.researchunitCENTRE FOR INFORMATICS AND SYSTEMS OF THE UNIVERSITY OF COIMBRA-
crisitem.author.orcid0000-0002-9770-7672-
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais
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