Modeling consumer loan default prediction using neural netware

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Modeling consumer loan default prediction using neural netware

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Modeling consumer loan default prediction using neural netware

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings - 2013 International Conference on Computer, Electrical and Electronics Engineering: 'Research Makes a Difference', ICCEEE 2013

ISBN

978-1-4673-6231-3

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

239-243

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Khartoum

Datum konání akce

26. 8. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000344372100042