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General Information
Editor-in-chief
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2010 Vol.2(4): 596-601 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2010.V2.208

Extracting Prediction Rules for Loan Default Using Neural Networks through Attribute Relevance Analysis

M. V. Jagannatha Reddy and B. Kavitha

Abstract—Predicting the class label loan defaulter using neural networks through attribute relevance analysis is presented in the previous paper. In this paper we are extracting prediction rules from the predicted class label. This method has the advantage that the number of units required can be reduced using attribute relevance analysis. So that we can increase the speed of neural network technique for predicting the class label of the new tuples. In this proposed paper attribute relevance analysis is used to eliminate irrelevant attributes to give as inputs to neural network. Neural networks used for predicting the class label and deriving the prediction rules from the class label. These rules are more useful in understanding the customer.

Index Terms—Classification rules, Attribute relevance analysis, neural networks, prediction rules, defaulter, classlabel.

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Cite: M. V. Jagannatha Reddy and B. Kavitha, "Extracting Prediction Rules for Loan Default Using Neural Networks through Attribute Relevance Analysis," International Journal of Computer Theory and Engineering vol. 2, no. 4, pp. 596-601, 2010. 

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