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A comparison of the classification a...

Juma, Sarah Awuor.

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A comparison of the classification accuracy of neural and neurofuzzy approaches in credit approval.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A comparison of the classification accuracy of neural and neurofuzzy approaches in credit approval./
Author:	Juma, Sarah Awuor.
Description:	81 p.
Notes:	Source: Masters Abstracts International, Volume: 44-04, page: 1977.
Contained By:	Masters Abstracts International44-04.
Subject:	Engineering, System Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1432089
ISBN:	9780542499845

A comparison of the classification accuracy of neural and neurofuzzy approaches in credit approval.
Juma, Sarah Awuor.

A comparison of the classification accuracy of neural and neurofuzzy approaches in credit approval. - 81 p.

Source: Masters Abstracts International, Volume: 44-04, page: 1977.

Thesis (M.S.)--State University of New York at Binghamton, 2006.

The increase in default rates and bankruptcy applications by credit customers in recent years has led financial institutions to seek better models for predicting default risks more accurately than the statistical models that have been used for many years. This has led to the use of artificial intelligence models, specifically neural networks and Neurofuzzy systems.

ISBN: 9780542499845Subjects--Topical Terms:

1018128
Engineering, System Science.

A comparison of the classification accuracy of neural and neurofuzzy approaches in credit approval.
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100 1 $a Juma, Sarah Awuor. $3 1912245
245 1 2 $a A comparison of the classification accuracy of neural and neurofuzzy approaches in credit approval.
300 $a 81 p.
500 $a Source: Masters Abstracts International, Volume: 44-04, page: 1977.
500 $a Adviser: Harold Lewis, III.
502 $a Thesis (M.S.)--State University of New York at Binghamton, 2006.
520 $a The increase in default rates and bankruptcy applications by credit customers in recent years has led financial institutions to seek better models for predicting default risks more accurately than the statistical models that have been used for many years. This has led to the use of artificial intelligence models, specifically neural networks and Neurofuzzy systems.
520 $a In this study we compare the classification accuracy of the multilayered perceptron neural network to that of the Neurofuzzy system ANFIS. The rules used for ANFIS training are derived from Classification Trees constructed using CART 5.0. The main objective is to classify credit applicants into two groups, high risk of default or low risk of default determined by relevant variables obtained from historical data.
520 $a The results obtained reveal that the neural network is a better classifier than CART based ANFIS based on the Root Mean Square Error measure. Classification accuracy measures however show that CART is superior to the neural network. We also hypothesize that it may be superior to ANFIS.
590 $a School code: 0792.
650 4 $a Engineering, System Science. $3 1018128
650 4 $a Artificial Intelligence. $3 769149
690 $a 0790
690 $a 0800
710 2 0 $a State University of New York at Binghamton. $3 737493
773 0 $t Masters Abstracts International $g 44-04.
790 1 0 $a Lewis, Harold, III, $e advisor
790 $a 0792
791 $a M.S.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1432089