Show simple item record

dc.contributor.authorGante, Dionicio D.
dc.contributor.authorGerardo, Bobby
dc.contributor.authorTanguilig, Bartolome T. III
dc.date.accessioned2022-03-09T05:07:45Z
dc.date.available2022-03-09T05:07:45Z
dc.date.issued2016
dc.identifier.citationGante, D. D., Gerardo, B. D., & Tanguilig, B. T. (2015). Neural network model using back propagation algorithm for credit risk evaluation. In 6th International Workshop on Computer Science and Engineering (WCSE 2016), Tokyo, Japan, 17-19 June, 2016, (pp. 500-506).en
dc.identifier.isbn978-981-11-0008-6
dc.identifier.urihttp://repository.wvsu.edu.ph/handle/123456789/77
dc.description.abstractThis paper present the results of an experiment made with the aid of KangarooBPNN, a graphical user interface software in order to find the Mean Squared Error (MSE) of a supervised neural network model. In the previous experimentation or study, NN-1B model was considered to be a good neural network model with 20 input neurons, 10 hidden neurons and 1 output neuron using 0.3 and 0.4 learning rate and accuracy rate respectively at 10,000 epochs. The German credit dataset was used to train and test the said neural network model using the back-propagation algorithm with momentum term for credit risk evaluation system. The results were recorded in a tabular form, compared and analyzed carefully. Then it was compared with the result of the previous study wherein the traditional back-propagation algorithm was used. Moreover, based on the comparison made by the researchers, it shows that it is better to use the back-propagation algorithm with momentum term than the traditional back-propagation algorithmen
dc.publisherThe SCIence and Engineering Institute (SCIEI)en
dc.relation.urien
dc.subjectneural networken
dc.subjectneural network modelen
dc.subjectback-propagation algorithmen
dc.subjectback-propagation algorithm with momentumen
dc.subjectcredit risk evaluationen
dc.subjectcredit scoringen
dc.subjectKangarooBPNNen
dc.subject.lcshNeural networks (Computer science)--Modelsen
dc.subject.lcshBack propagation (Artificial intelligence)en
dc.subject.lcshCredit scoring systemsen
dc.titleNeural network model using back-propagation algorithm with momentum term for credit risk evaluation systemen
dc.typeConference paperen
dc.identifier.doi10.18178/wcse.2016.06.084
dc.citation.conferencetitle6th International Workshop on Computer Science and Engineering, WCSE 2016en


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record