About: Modeling consumer loan default prediction using neural netware

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Modeling consumer loan default prediction using neural netware Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
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. 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. (en)
Title	Modeling consumer loan default prediction using neural netware Modeling consumer loan default prediction using neural netware (en)
skos:prefLabel	Modeling consumer loan default prediction using neural netware Modeling consumer loan default prediction using neural netware (en)
skos:notation	RIV/61989100:27240/13:86092836!RIV15-MSM-27240___
http://linked.open...avai/riv/aktivita	S
http://linked.open...avai/riv/aktivity	S
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Abraham Padath, Ajith
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoká škola báňská - Technická univerzita Ostrava / Fakulta elektrotechniky a informatiky
http://linked.open...dnocenehoVysledku	88833
http://linked.open...ai/riv/idVysledku	RIV/61989100:27240/13:86092836
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	scaled conjugate gradient backpropagation; neural network; loan default; Levenberg-Marquardt algorithm and One-step secant backpropagation; credit risk (en)
http://linked.open.../riv/klicoveSlovo	credit risk neural network Levenberg-Marquardt algorithm and One-step secant backpropagation loan default scaled conjugate gradient backpropagation
http://linked.open...ontrolniKodProRIV	[EEFCECC1A17F]
http://linked.open...v/mistoKonaniAkce	Khartoum
http://linked.open...i/riv/mistoVydani	New York
http://linked.open...i/riv/nazevZdroje	Proceedings - 2013 International Conference on Computer, Electrical and Electronics Engineering: 'Research Makes a Difference', ICCEEE 2013
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Abraham Padath, Ajith Hassan, A.K.I.
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2013-08-26 (xsd:date)
number of pages	5 (xsd:int)
http://bibframe.org/vocab/doi	10.1109/ICCEEE.2013.6633940
http://purl.org/ne...btex#hasPublisher	IEEE
https://schema.org/isbn	978-1-4673-6231-3
http://localhost/t...ganizacniJednotka	27240

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software