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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • 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

  • e-ISSN

  • 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