Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/44341
DC FieldValueLanguage
dc.contributor.authorAghaeiRad, Ali-
dc.contributor.authorChen, Ning-
dc.contributor.authorRibeiro, Bernardete-
dc.date.accessioned2017-11-10T16:27:08Z-
dc.date.available2017-11-10T16:27:08Z-
dc.date.issued2016-
dc.identifier.urihttp://hdl.handle.net/10316/44341-
dc.description.abstractCredit scoring is important for credit risk evaluation and monitoring in the accounting and finance domain. For financial institutions, the ability to predict the business failure is crucial, as incorrect decisions have direct financial consequences. A variety of pattern recognition techniques including neural networks, decision trees, and support vector machines have been applied to predict whether the borrowers should be considered a good or bad credit risk. This paper presents a hybrid approach to building the credit scoring model and illustrates how the unsupervised learning based on self-organizing map (SOM) can improve the discriminant capability of feedforward neural network (FNN). Within the hybridization scheme, the knowledge (i.e., prototypes of clusters) found by SOM is transferred as input to the subsequent FNN model. Four real-world data sets are used in the experiments for credit approval problems. By varying the parameters, the experimental results demonstrate the predictive model built by the hybrid approach can achieve better performance than the stand-alone FNN particularly when a limited amount of labeled data is available. This gives some insights on how to construct more accurate predictive models when the data collection is difficult in some financial applications. A complete and unique graphical visualization technique is shown which better outlines the trade-off between distinct metrics and attained performance.por
dc.language.isoengpor
dc.rightsopenAccesspor
dc.subjectCredit scoringpor
dc.subjectSelf-organizing mappor
dc.subjectHybrid classificationpor
dc.titleImprove credit scoring using transfer of learned knowledge from self-organizing mappor
dc.typearticle-
degois.publication.firstPage1329por
degois.publication.lastPage1342por
degois.publication.issue6por
degois.publication.titleNeural Computing and Applicationspor
dc.peerreviewedyespor
dc.identifier.doi10.1007/s00521-016-2567-2por
dc.identifier.doi10.1007/s00521-016-2567-2-
degois.publication.volume28por
uc.controloAutoridadeSim-
item.languageiso639-1en-
item.fulltextCom Texto completo-
item.grantfulltextopen-
crisitem.author.deptFaculty of Sciences and Technology-
crisitem.author.parentdeptUniversity of Coimbra-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.orcid0000-0002-9770-7672-
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais
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