Modeling Consumer Loan Default Prediction Using Ensemble Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86089361" target="_blank" >RIV/61989100:27240/13:86089361 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/13:86089361
Result on the web
<a href="http://dx.doi.org/10.1109/ICCEEE.2013.6634029" target="_blank" >http://dx.doi.org/10.1109/ICCEEE.2013.6634029</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICCEEE.2013.6634029" target="_blank" >10.1109/ICCEEE.2013.6634029</a>
Alternative languages
Result language
angličtina
Original language name
Modeling Consumer Loan Default Prediction Using Ensemble Neural Networks
Original language description
In this paper, a loan default prediction model is constricted using three different training algorithms, to train a supervised two-layer feed-forward network to produce the prediction model. But first, two attribute filtering functions were used, resulting in two data sets with reduced attributes and the original data-set. Back propagation based learning algorithms was used for training the network. The neural networks are trained using real world credit application cases from a German bank datasets which has 1000 cases; each case with 24 numerical attributes; upon, which the decision is based. The aim of this paper was to compare between the resulting models produced from using different training algorithms, scaled conjugate gradient backpropagation,Levenberg-Marquardt algorithm, One-step secant backpropagation (SCG, LM and OSS) and an ensemble of SCG, LM and OSS. Empirical results indicate that training algorithms improve the design of a loan default prediction model and ensemble mo
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings - 2013 International Conference on Computer, Electrical and Electronics Engineering: 'Research Makes a Difference', ICCEEE 2013
ISBN
978-1-4673-6231-3
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
719-724
Publisher name
IEEE
Place of publication
New York
Event location
Khartoum
Event date
Aug 26, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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