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In order to more closely approximate the training and testing methodology utilized by neural networks, the jackknife method of discriminant analysis was utilized. This "leave-one-out" principle is a sophisticated method based on estimation with multiple subsets of the sample. In other words, the discriminant function is fitted to repeatedly drawn samples of the original sample. The jackknife method yielded 50.4% of original grouped cases correctly classified. These results are shown in Table 1.

Table 1: Predicted group membership

No Fraud Detected

Fraud Detected

No Fraud Detected



Fraud Detected






50.4% of grouped cases correctly classified

172 + 30/400 = 50.4%

123+75/400 = 49.5%

In comparison, the neural network method classified 75.9% good parts. The training set consisted of 200 respondents. The data was normalized and ran for one hour and 20 minutes before it resulted in 100 good parts. A weight was obtained, and the network was saved. The testing data set (composed of 210 respondents) was then placed into the saved network and run. After 24 hours, the neural network had achieved 75.9% good parts. These results of the neural net testing are shown in Table 2.

Table 2: Percent good parts of neural net testing

Fraud/No Fraud

Correctly Identified


Incorrectly Identified




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Managing Data Mining Technologies in Organizations(c) Techniques and Applications
Managing Data Mining Technologies in Organizations: Techniques and Applications
ISBN: 1591400570
EAN: 2147483647
Year: 2003
Pages: 174 © 2008-2017.
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