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.
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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