Modeling consumer loan default prediction using neural netware
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86092836" target="_blank" >RIV/61989100:27240/13:86092836 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICCEEE.2013.6633940" target="_blank" >http://dx.doi.org/10.1109/ICCEEE.2013.6633940</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICCEEE.2013.6633940" target="_blank" >10.1109/ICCEEE.2013.6633940</a>
Alternative languages
Result language
angličtina
Original language name
Modeling consumer loan default prediction using neural netware
Original language description
In this paper a loan default prediction model was constricted using two attribute detection functions, resulting in two data-sets with reduced attributes and the original data-set. A supervised two-layer feed-forward network, with sigmoid hidden neurons and output neurons is used to produce the prediction model. Back propagation learning algorithm was used for the network. Furthermore three different training algorithms were used to train the neural networks. The neural networks are trained using real world credit application cases from the 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 and One-step secant backpropagation (SCG, LM and OSS). This study show that although there is no great difference between LM and SCG but still LM gives better results. The attribute reduction function used helped to produced models quickly and more accurately. 2013 IEEE.
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
5
Pages from-to
239-243
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
000344372100042