All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

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

  • 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

    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