Show simple item record

2012-04-27Buch DOI: 10.18452/4403
Support Vector Machines with Evolutionary Feature Selection for Default Prediction
dc.contributor.authorHärdle, Wolfgang Karl
dc.contributor.authorPrastyo, Dedy Dwi
dc.contributor.authorHafner, Christian
dc.date.accessioned2017-06-16T00:39:08Z
dc.date.available2017-06-16T00:39:08Z
dc.date.created2012-05-10
dc.date.issued2012-04-27
dc.date.submitted2012-04-27
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/5055
dc.description.abstractPredicting default probabilities is at the core of credit risk management and is becoming more and more important for banks in order to measure their client's degree of risk, and for firms to operate successfully. The SVM with evolutionary feature selection is applied to the CreditReform database. We use classical methods such as discriminan analysis (DA), logit and probit models as benchmark On overall, GA-SVM is outperforms compared to the benchmark models in both training and testing dataset.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.subjectSVMeng
dc.subjectGenetic algorithmeng
dc.subjectglobal optmimumeng
dc.subjectdefault predictioneng
dc.subject.ddc330 Wirtschaft
dc.titleSupport Vector Machines with Evolutionary Feature Selection for Default Prediction
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-100201707
dc.identifier.doihttp://dx.doi.org/10.18452/4403
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages26
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2012
local.edoc.container-issue30
local.edoc.container-year2012
local.edoc.container-erstkatid2195055-6

Show simple item record