Modeling Consumer Loan Default Prediction Using Ensemble Neural Networks

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27740/13:86089361

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Modeling Consumer Loan Default Prediction Using Ensemble Neural Networks

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Modeling Consumer Loan Default Prediction Using Ensemble Neural Networks

Popis výsledku anglicky

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

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

6

Strana od-do

719-724

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

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